diff --git a/README.md b/README.md
index 9c30bdbc..eec50a13 100644
--- a/README.md
+++ b/README.md
@@ -10,9 +10,9 @@ This repository contains the code for optimization and machine learning algorith
 
 ## Coding Philosophy
 As much as we can, we follow the [CleanRL](https://github.com/vwxyzjn/cleanrl) philosophy: single-file, high-quality implementations with research-friendly features:
-* Single-file implementation: every detail is in one file, so you can easily understand and modify the code.
+* Single-file implementation: every training detail is in one file, so you can easily understand and modify the code. There is usually another file that contains evaluation code.
 * High-quality: we use type hints, docstrings, and comments to make the code easy to understand. We also rely on linters for formatting and checking our code.
-* Logging: we use experiment tracking tools like [Weights & Biases](https://wandb.ai/site) to log the results of our experiments.
+* Logging: we use experiment tracking tools like [Weights & Biases](https://wandb.ai/site) to log the results of our experiments. All our "official" runs are logged in the [EngiOpt project](https://wandb.ai/engibench/engiopt).
 * Reproducibility: we seed all the random number generators, make PyTorch deterministic, report the hyperparameters and code in WandB.
 
 ## Install
@@ -34,3 +34,7 @@ pip install -e ".[all]"
 ## Dashboards
 The integration with WandB allows us to access live dashboards of our runs (on the cluster or not). We also upload the trained models there. You can access some of our runs at https://wandb.ai/engibench/engiopt.
 <img src="imgs/wandb_dashboard.png" alt="WandB dashboards"/>
+
+## Colab notebooks
+We have some colab notebooks that show how to use some of the EngiBench/EngiOpt features.
+* [Example hard model](https://colab.research.google.com/github/IDEALLab/EngiOpt/blob/main/example_hard_model.ipynb)
diff --git a/example_hard_model.ipynb b/example_hard_model.ipynb
new file mode 100644
index 00000000..4c4f3ad8
--- /dev/null
+++ b/example_hard_model.ipynb
@@ -0,0 +1,6914 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Playing with a model\n",
+    "\n",
+    "In this notebook, we will use a Conditional Diffusion model to generate designs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preliminaries\n",
+    "\n",
+    "First let's make sure we have the necessary packages installed. Be sure to create a virtual environment, then install the required packages for this project in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -e ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Some imports we will need\n",
+    "import os\n",
+    "\n",
+    "from diffusers import UNet2DConditionModel\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch as th\n",
+    "\n",
+    "from engiopt import metrics\n",
+    "from engiopt.diffusion_2d_cond.diffusion_2d_cond import beta_schedule\n",
+    "from engiopt.diffusion_2d_cond.diffusion_2d_cond import DiffusionSampler\n",
+    "import wandb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's make sure we can interact with the problem in EngiBench."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 2 Axes>, <Axes: >)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from engibench.problems.heatconduction2d import HeatConduction2D\n",
+    "\n",
+    "problem = HeatConduction2D()\n",
+    "design, _ = problem.random_design()\n",
+    "problem.render(design)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training a model\n",
+    "\n",
+    "In EngiOpt, we have a collection of models that can be used to generate designs.\n",
+    "\n",
+    "Each model is defined under a dedicated folder in the `engiopt` package.\n",
+    "\n",
+    "For example, the `diffusion_2d_cond` folder contains the code for the Conditional Diffusion model.\n",
+    "\n",
+    "In each model folder, there are two scripts; one for training the model, and one for evaluating the model.\n",
+    "\n",
+    "To train a model, we can use the `diffusion_2d_cond.py` script. There is a command line interface that can be used to specify the model parameters.\n",
+    "\n",
+    "To see the available options, we can use the `-h` flag.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/ffelte/Documents/EngiLearn/.venv/lib/python3.12/site-packages/gymnasium/spaces/box.py:235: UserWarning: \u001b[33mWARN: Box low's precision lowered by casting to float32, current low.dtype=float64\u001b[0m\n",
+      "  gym.logger.warn(\n",
+      "/Users/ffelte/Documents/EngiLearn/.venv/lib/python3.12/site-packages/gymnasium/spaces/box.py:305: UserWarning: \u001b[33mWARN: Box high's precision lowered by casting to float32, current high.dtype=float64\u001b[0m\n",
+      "  gym.logger.warn(\n",
+      "\u001b[1musage\u001b[0m: diffusion_2d_cond.py [-h] [OPTIONS]\n",
+      "\n",
+      "Command-line arguments.\n",
+      "\n",
+      "\u001b[2m╭─\u001b[0m\u001b[2m options \u001b[0m\u001b[2m─────────────────────────────────────────────────────────────────\u001b[0m\u001b[2m─╮\u001b[0m\n",
+      "\u001b[2m│\u001b[0m -h, --help              \u001b[2mshow this help message and exit\u001b[0m                    \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --problem-id \u001b[1mSTR\u001b[0m        \u001b[2mProblem identifier.\u001b[0m \u001b[36m(default: beams2d)\u001b[0m             \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --algo \u001b[1mSTR\u001b[0m              \u001b[2mThe name of this algorithm.\u001b[0m \u001b[36m(default: \u001b[0m             \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m                         \u001b[36mdiffusion_2d_cond)\u001b[0m                                 \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --track, --no-track     \u001b[2mTrack the experiment with wandb.\u001b[0m \u001b[36m(default: True)\u001b[0m   \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --wandb-project \u001b[1mSTR\u001b[0m     \u001b[2mWandb project name.\u001b[0m \u001b[36m(default: engiopt)\u001b[0m             \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --wandb-entity \u001b[1m{None}|STR\u001b[0m                                                  \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m                         \u001b[2mWandb entity name.\u001b[0m \u001b[36m(default: None)\u001b[0m                 \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --seed \u001b[1mINT\u001b[0m              \u001b[2mRandom seed.\u001b[0m \u001b[36m(default: 1)\u001b[0m                          \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --save-model, --no-save-model                                              \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m                         \u001b[2mSaves the model to disk.\u001b[0m \u001b[36m(default: False)\u001b[0m          \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --n-epochs \u001b[1mINT\u001b[0m          \u001b[2mnumber of epochs of training\u001b[0m \u001b[36m(default: 200)\u001b[0m        \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --batch-size \u001b[1mINT\u001b[0m        \u001b[2msize of the batches\u001b[0m \u001b[36m(default: 32)\u001b[0m                  \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --lr \u001b[1mFLOAT\u001b[0m              \u001b[2mlearning rate\u001b[0m \u001b[36m(default: 0.0004)\u001b[0m                    \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --b1 \u001b[1mFLOAT\u001b[0m              \u001b[2mdecay of first order momentum of gradient\u001b[0m          \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m                         \u001b[36m(default: 0.5)\u001b[0m                                     \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --b2 \u001b[1mFLOAT\u001b[0m              \u001b[2mdecay of first order momentum of gradient\u001b[0m          \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m                         \u001b[36m(default: 0.999)\u001b[0m                                   \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --n-cpu \u001b[1mINT\u001b[0m             \u001b[2mnumber of cpu threads to use during batch \u001b[0m         \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m                         \u001b[2mgeneration\u001b[0m \u001b[36m(default: 8)\u001b[0m                            \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --latent-dim \u001b[1mINT\u001b[0m        \u001b[2mdimensionality of the latent space\u001b[0m \u001b[36m(default: 100)\u001b[0m  \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --sample-interval \u001b[1mINT\u001b[0m   \u001b[2minterval between image samples\u001b[0m \u001b[36m(default: 400)\u001b[0m      \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --num-timesteps \u001b[1mINT\u001b[0m     \u001b[2mNumber of timesteps in the diffusion schedule\u001b[0m      \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m                         \u001b[36m(default: 250)\u001b[0m                                     \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --layers-per-block \u001b[1mINT\u001b[0m  \u001b[2mLayers per U-NET block\u001b[0m \u001b[36m(default: 2)\u001b[0m                \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m --noise-schedule \u001b[1m{linear,cosine,exp}\u001b[0m                                       \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m                         \u001b[2mDiffusion schedule ('linear', 'cosine', 'exp')\u001b[0m     \u001b[2m│\u001b[0m\n",
+      "\u001b[2m│\u001b[0m                         \u001b[36m(default: linear)\u001b[0m                                  \u001b[2m│\u001b[0m\n",
+      "\u001b[2m╰────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "!python engiopt/diffusion_2d_cond/diffusion_2d_cond.py -h"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's say we want to train the model for the heat conduction problem, use weights and biases to track the training process, and save the resulting model in wandb. The resulting command would look like this (will take a while to run):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/ffelte/Documents/EngiLearn/.venv/lib/python3.12/site-packages/gymnasium/spaces/box.py:235: UserWarning: \u001b[33mWARN: Box low's precision lowered by casting to float32, current low.dtype=float64\u001b[0m\n",
+      "  gym.logger.warn(\n",
+      "/Users/ffelte/Documents/EngiLearn/.venv/lib/python3.12/site-packages/gymnasium/spaces/box.py:305: UserWarning: \u001b[33mWARN: Box high's precision lowered by casting to float32, current high.dtype=float64\u001b[0m\n",
+      "  gym.logger.warn(\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mflorian-felten\u001b[0m (\u001b[33mengibench\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.19.10\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/Users/ffelte/Documents/EngiLearn/wandb/run-20250709_140832-j9xi9nsl\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mheatconduction2d__diffusion_2d_cond__1__1752062911\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/engibench/engiopt\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/engibench/engiopt/runs/j9xi9nsl\u001b[0m\n",
+      "  0%|                                                   | 0/200 [00:00<?, ?it/s][Epoch 0/200] [Batch 0/12] [loss: 1.0207675695419312]] [2.69 sec]\n",
+      "[Epoch 0/200] [Batch 1/12] [loss: 0.6981420516967773]] [1.47 sec]\n",
+      "[Epoch 0/200] [Batch 2/12] [loss: 0.49842676520347595]] [1.13 sec]\n",
+      "[Epoch 0/200] [Batch 3/12] [loss: 0.3593920171260834]] [1.13 sec]\n",
+      "[Epoch 0/200] [Batch 4/12] [loss: 0.3656233251094818]] [1.13 sec]\n",
+      "[Epoch 0/200] [Batch 5/12] [loss: 0.2464381903409958]] [1.14 sec]\n",
+      "[Epoch 0/200] [Batch 6/12] [loss: 0.19566160440444946]] [1.13 sec]\n",
+      "[Epoch 0/200] [Batch 7/12] [loss: 0.1394335925579071]] [1.14 sec]\n",
+      "[Epoch 0/200] [Batch 8/12] [loss: 0.1974942535161972]] [1.13 sec]\n",
+      "[Epoch 0/200] [Batch 9/12] [loss: 0.153664231300354]] [1.13 sec]\n",
+      "[Epoch 0/200] [Batch 10/12] [loss: 0.14867500960826874]] [1.13 sec]\n",
+      "[Epoch 0/200] [Batch 11/12] [loss: 0.18152955174446106]] [1.73 sec]\n",
+      "  0%|▏                                        | 1/200 [01:22<4:33:04, 82.33s/it][Epoch 1/200] [Batch 0/12] [loss: 0.17296424508094788]] [1.15 sec]\n",
+      "[Epoch 1/200] [Batch 1/12] [loss: 0.11520194262266159]] [1.13 sec]\n",
+      "[Epoch 1/200] [Batch 2/12] [loss: 0.1831229329109192]] [1.16 sec]\n",
+      "[Epoch 1/200] [Batch 3/12] [loss: 0.18641111254692078]] [1.16 sec]\n",
+      "[Epoch 1/200] [Batch 4/12] [loss: 0.1475542038679123]] [1.13 sec]\n",
+      "[Epoch 1/200] [Batch 5/12] [loss: 0.16754433512687683]] [1.15 sec]\n",
+      "^C\n",
+      "  0%|▏                                        | 1/200 [01:30<4:59:16, 90.23s/it]\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/ffelte/Documents/EngiLearn/engiopt/diffusion_2d_cond/diffusion_2d_cond.py\", line 382, in <module>\n",
+      "    optimizer.step()\n",
+      "  File \"/Users/ffelte/Documents/EngiLearn/.venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 487, in wrapper\n",
+      "    out = func(*args, **kwargs)\n",
+      "          ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/ffelte/Documents/EngiLearn/.venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 91, in _use_grad\n",
+      "    ret = func(self, *args, **kwargs)\n",
+      "          ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/ffelte/Documents/EngiLearn/.venv/lib/python3.12/site-packages/torch/optim/adamw.py\", line 220, in step\n",
+      "    adamw(\n",
+      "  File \"/Users/ffelte/Documents/EngiLearn/.venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 154, in maybe_fallback\n",
+      "    return func(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/ffelte/Documents/EngiLearn/.venv/lib/python3.12/site-packages/torch/optim/adamw.py\", line 782, in adamw\n",
+      "    func(\n",
+      "  File \"/Users/ffelte/Documents/EngiLearn/.venv/lib/python3.12/site-packages/torch/optim/adamw.py\", line 376, in _single_tensor_adamw\n",
+      "    exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\n",
+      "    ^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python engiopt/diffusion_2d_cond/diffusion_2d_cond.py --problem-id \"heatconduction2d\" --track --wandb-entity None --save-model --n-epochs 200 --seed 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Restoring a model\n",
+    "\n",
+    "After training a model, we can: \n",
+    "1. Download a model from wandb\n",
+    "2. Instantiade the model and its sampler from the artifact"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Downloading a model from wandb\n",
+    "problem_id = \"heatconduction2d\"\n",
+    "seed = 1\n",
+    "\n",
+    "# This is the name of the model in wandb, for the sake of the example we will use a model that is stored on the official engiopt wandb project. If you want to use a model that you trained yourself, you can change the artifact_path to the path of your model.\n",
+    "artifact_path = f\"engibench/engiopt/{problem_id}_diffusion_2d_cond_model:seed_{seed}\"\n",
+    "\n",
+    "api = wandb.Api()\n",
+    "artifact = api.artifact(artifact_path, type=\"model\")\n",
+    "artifact_dir = artifact.download()\n",
+    "ckpt_path = os.path.join(artifact_dir, \"model.pth\")\n",
+    "\n",
+    "if th.cuda.is_available():\n",
+    "    device = th.device(\"cuda\")\n",
+    "elif th.backends.mps.is_available():\n",
+    "    device = th.device(\"mps\")\n",
+    "else:\n",
+    "    device = th.device(\"cpu\")\n",
+    "\n",
+    "ckpt = th.load(ckpt_path, map_location=device)\n",
+    "\n",
+    "# We will also need the \"run\" object to get the model configuration which was used to train the model.\n",
+    "run = artifact.logged_by()\n",
+    "\n",
+    "# This is the model definition that was used to train the model. You can find the definition in the engiopt/diffusion_2d_cond/diffusion_2d_cond.py file.\n",
+    "model = UNet2DConditionModel(\n",
+    "    sample_size=problem.design_space.shape,\n",
+    "    in_channels=1,\n",
+    "    out_channels=1,\n",
+    "    cross_attention_dim=64,\n",
+    "    block_out_channels=(32, 64, 128, 256),\n",
+    "    down_block_types=(\"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"DownBlock2D\"),\n",
+    "    up_block_types=(\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\"),\n",
+    "    layers_per_block=run.config[\"layers_per_block\"],\n",
+    "    transformer_layers_per_block=1,\n",
+    "    encoder_hid_dim=len(problem.conditions),\n",
+    "    only_cross_attention=True,\n",
+    ").to(device)\n",
+    "\n",
+    "model.load_state_dict(ckpt[\"model\"])\n",
+    "model.eval()\n",
+    "\n",
+    "# The noise schedule must also be restored\n",
+    "options = {\n",
+    "    \"cosine\": run.config[\"noise_schedule\"] == \"cosine\",\n",
+    "    \"exp_biasing\": run.config[\"noise_schedule\"] == \"exp\",\n",
+    "    \"exp_bias_factor\": 1,\n",
+    "}\n",
+    "betas = beta_schedule(\n",
+    "    t=run.config[\"num_timesteps\"],\n",
+    "    start=1e-4,\n",
+    "    end=0.02,\n",
+    "    scale=1.0,\n",
+    "    options=options,\n",
+    ")\n",
+    "ddm_sampler = DiffusionSampler(run.config[\"num_timesteps\"], betas)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sampling from a model\n",
+    "\n",
+    "Now we can use the model to generate designs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_samples = 3\n",
+    "\n",
+    "conditions = [\n",
+    "    {\"volume\": 0.3, \"length\": 0.5},\n",
+    "    {\"volume\": 0.4, \"length\": 0.7},\n",
+    "    {\"volume\": 0.2, \"length\": 0.1},\n",
+    "]\n",
+    "\n",
+    "conditions_tensor = th.tensor([list(c.values()) for c in conditions], device=device)\n",
+    "conditions_tensor = conditions_tensor.unsqueeze(1)  # Add channel dim\n",
+    "\n",
+    "design_shape = problem.design_space.shape\n",
+    "# Denoise the designs in batch\n",
+    "gen_designs = th.randn((n_samples, 1, *design_shape), device=device)\n",
+    "for i in reversed(range(run.config[\"num_timesteps\"])):\n",
+    "    t = th.full((n_samples,), i, device=device, dtype=th.long)\n",
+    "    gen_designs = ddm_sampler.sample_timestep(model, gen_designs, t, conditions_tensor)\n",
+    "\n",
+    "gen_designs = gen_designs.cpu().numpy()\n",
+    "gen_designs = gen_designs.squeeze()  # Remove the channel dim\n",
+    "\n",
+    "# Render the designs\n",
+    "for i in range(n_samples):\n",
+    "    problem.render(gen_designs[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluating the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optimizing generated designs\n",
+    "\n",
+    "Let's first optimize the generated designs, to see how far away they are from optimal designs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.086e-17 (tol = 1.000e-07) r (rel) = 5.134e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "List of options:\n",
+      "\n",
+      "                                    Name   Value                # times used\n",
+      "                          acceptable_tol = 0.001                     1\n",
+      "                        file_print_level = 5                         1\n",
+      "                   hessian_approximation = limited-memory            7\n",
+      "                                max_iter = 100                       1\n",
+      "                             output_file = /home/fenics/shared/templates/RES_OPT/solution_V=0.3_w=0.5.txt      1\n",
+      "                             print_level = 6                         2\n",
+      "\n",
+      "******************************************************************************\n",
+      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
+      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
+      "         For more information visit http://projects.coin-or.org/Ipopt\n",
+      "******************************************************************************\n",
+      "\n",
+      "This is Ipopt version 3.12.9, running with linear solver mumps.\n",
+      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
+      "\n",
+      "Number of nonzeros in equality constraint Jacobian...:        0\n",
+      "Number of nonzeros in inequality constraint Jacobian.:    10201\n",
+      "Number of nonzeros in Lagrangian Hessian.............:        0\n",
+      "\n",
+      "Hessian approximation will be done in the space of all 10201 x variables.\n",
+      "\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Scaling parameter for objective function = 1.000000e+00\n",
+      "objective scaling factor = 1\n",
+      "No x scaling provided\n",
+      "No c scaling provided\n",
+      "No d scaling provided\n",
+      "Moved initial values of x sufficiently inside the bounds.\n",
+      "Initial values of s sufficiently inside the bounds.\n",
+      "MUMPS used permuting_scaling 5 and pivot_order 5.\n",
+      "           scaling will be 77.\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Least square estimates max(y_c) = 0.000000e+00, max(y_d) = 9.999011e-01\n",
+      "Total number of variables............................:    10201\n",
+      "                     variables with only lower bounds:        0\n",
+      "                variables with lower and upper bounds:    10201\n",
+      "                     variables with only upper bounds:        0\n",
+      "Total number of equality constraints.................:        0\n",
+      "Total number of inequality constraints...............:        1\n",
+      "        inequality constraints with only lower bounds:        1\n",
+      "   inequality constraints with lower and upper bounds:        0\n",
+      "        inequality constraints with only upper bounds:        0\n",
+      "\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.163e-17 (tol = 1.000e-07) r (rel) = 5.212e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "Limited-Memory approximation started; store data at current iterate.\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 0:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   0  1.8572449e-04 0.00e+00 1.00e-04   0.0 0.00e+00    -  0.00e+00 0.00e+00   0 y\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 0 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.0000000000000000e+00\n",
+      "Current fraction-to-the-boundary parameter tau = 0.0000000000000000e+00\n",
+      "\n",
+      "||curr_x||_inf   = 8.2412612438201904e-01\n",
+      "||curr_s||_inf   = 1.1129260906092758e-01\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 9.9990112949266385e-01\n",
+      "||curr_z_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_z_U||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 0:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.8572448735931820e-04    1.8572448735931820e-04\n",
+      "Dual infeasibility......:   1.0001366584133642e-04    1.0001366584133642e-04\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.9000001999999998e-01    9.9000001999999998e-01\n",
+      "Overall NLP error.......:   9.9000001999999998e-01    9.9000001999999998e-01\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "Setting mu_max to 4.999810e+02.\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 1 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 1.000000e-06\n",
+      "Barrier parameter mu computed by oracle is 4.999810e-07\n",
+      "Barrier parameter mu after safeguards is 4.999810e-07\n",
+      "Barrier Parameter: 4.999810e-07\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 4.033025e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.115802e-16\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 0 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 0.000000E+00\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "trial_max is initialized to 1.000000e+04\n",
+      "trial_min is initialized to 1.000000e-04\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.031e-17 (tol = 1.000e-07) r (rel) = 5.079e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  1.7761639178031554e-02  (reference  1.7761788605270844e-02):\n",
+      "  New values of constraint violation =  6.5225602696727947e-16  (reference  0.0000000000000000e+00):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 7.065045e-07\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.375760e-11 snrm = 3.988065e-05 ynrm = 6.903371e-07\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 8.650042e-03\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 1:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   1  1.8572445e-04 0.00e+00 1.21e-06  -6.3 7.07e-07    -  9.90e-01 1.00e+00f  1  sigma=1.00e-06 qf=13y \n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 1 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 4.9998095851772871e-07\n",
+      "Current fraction-to-the-boundary parameter tau = 9.8999999999999999e-01\n",
+      "\n",
+      "||curr_x||_inf   = 8.2412596215053446e-01\n",
+      "||curr_s||_inf   = 1.1129228096813605e-01\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.0006971437128736e-02\n",
+      "||curr_z_L||_inf = 1.0001740723567520e-02\n",
+      "||curr_z_U||_inf = 1.0005053859429958e-02\n",
+      "||curr_v_L||_inf = 1.0007638643969696e-02\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 1:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.8572445253169502e-04    1.8572445253169502e-04\n",
+      "Dual infeasibility......:   1.2092250787718722e-06    1.2092250787718722e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.9012414945559273e-03    9.9012414945559273e-03\n",
+      "Overall NLP error.......:   9.9012414945559273e-03    9.9012414945559273e-03\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "Switching to fixed mu mode with mu =   4.0003526782451376e-03 and tau =   9.9599964732175483e-01.\n",
+      "Barrier Parameter: 4.000353e-03\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 1.030212e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.981177e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 1 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 3.394411E-28\n",
+      "Starting checks for alpha (primal) = 4.51e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.806e-17 (tol = 1.000e-07) r (rel) = 4.852e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 4.507222e-01:\n",
+      "  New values of barrier function     =  7.0390390173741793e+01  (reference  1.4062525623354958e+02):\n",
+      "  New values of constraint violation =  9.7144514654701197e-16  (reference  6.5225602696727947e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.130631e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = -1.790153e-06 snrm = 1.499971e+01 ynrm = 1.679536e-05\n",
+      "     Skip the update.\n",
+      "Number of successive iterations with skipping: 1\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 2:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   2  2.4051126e-04 0.00e+00 1.54e-02  -2.4 6.95e-01    -  5.91e-01 4.51e-01f  1 FNhj y Ws\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 2 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 4.0003526782451376e-03\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9599964732175483e-01\n",
+      "\n",
+      "||curr_x||_inf   = 6.9631634276192933e-01\n",
+      "||curr_s||_inf   = 4.4519841424242590e-04\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 3.9457009625083139e-02\n",
+      "||curr_z_L||_inf = 1.9963021675158434e-02\n",
+      "||curr_z_U||_inf = 1.1964978032678477e-02\n",
+      "||curr_v_L||_inf = 3.8416859498239253e-02\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 2:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   2.4051126086857644e-04    2.4051126086857644e-04\n",
+      "Dual infeasibility......:   1.5405266669304682e-02    1.5405266669304682e-02\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   7.1804208850246984e-03    7.1804208850246984e-03\n",
+      "Overall NLP error.......:   1.5405266669304682e-02    1.5405266669304682e-02\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Reducing mu to   2.5301567154702848e-04 in fixed mu mode. Tau becomes   9.9599964732175483e-01\n",
+      "Barrier Parameter: 2.530157e-04\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 1.863863e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 3.640358e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 2 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 5.357732E-24\n",
+      "Starting checks for alpha (primal) = 5.92e-02\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.916e-17 (tol = 1.000e-07) r (rel) = 4.962e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 5.922937e-02:\n",
+      "  New values of barrier function     =  4.4480923244584814e+00  (reference  4.4523007218524020e+00):\n",
+      "  New values of constraint violation =  5.1309867812876497e-16  (reference  9.7144514654701197e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.057858e-03\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 6.031093e-10 snrm = 6.938498e-02 ynrm = 1.499893e-07\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.252752e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 3:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   3  2.4102359e-04 0.00e+00 8.05e-04  -3.6 3.47e-02    -  1.00e+00 5.92e-02f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 3 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 2.5301567154702848e-04\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9599964732175483e-01\n",
+      "\n",
+      "||curr_x||_inf   = 6.9558595276290758e-01\n",
+      "||curr_s||_inf   = 1.7709906722919813e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.2143630829368719e+00\n",
+      "||curr_z_L||_inf = 1.2658655007020073e-03\n",
+      "||curr_z_U||_inf = 7.9478026811410918e-04\n",
+      "||curr_v_L||_inf = 1.2143253677295873e+00\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 3:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   2.4102358960018843e-04    2.4102358960018843e-04\n",
+      "Dual infeasibility......:   8.0472502989564038e-04    8.0472502989564038e-04\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   5.5994619120106461e-04    5.5994619120106461e-04\n",
+      "Overall NLP error.......:   8.0472502989564038e-04    8.0472502989564038e-04\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Reducing mu to   4.0245852639214270e-06 in fixed mu mode. Tau becomes   9.9974698432845299e-01\n",
+      "Barrier Parameter: 4.024585e-06\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 3.283049e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 2.875270e-19\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 3 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.027775E-21\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.758e-17 (tol = 1.000e-07) r (rel) = 4.803e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  7.0747106848393596e-02  (reference  7.0990620609180072e-02):\n",
+      "  New values of constraint violation =  2.7761504846259694e-16  (reference  5.1309867812876497e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 6.726803e-03\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 3.114592e-07 snrm = 2.997108e-01 ynrm = 1.587526e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 3.467341e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 4:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   4  2.4871288e-04 0.00e+00 1.06e-06  -5.4 6.73e-03    -  1.00e+00 1.00e+00f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 4 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 4.0245852639214270e-06\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9974698432845299e-01\n",
+      "\n",
+      "||curr_x||_inf   = 6.9091064219368747e-01\n",
+      "||curr_s||_inf   = 4.9621469914718535e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 8.3932593228814703e-02\n",
+      "||curr_z_L||_inf = 1.4675535495221209e-05\n",
+      "||curr_z_U||_inf = 1.0611938704661796e-05\n",
+      "||curr_v_L||_inf = 8.3932608674677001e-02\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 4:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   2.4871287737524418e-04    2.4871287737524418e-04\n",
+      "Dual infeasibility......:   1.0631618153561819e-06    1.0631618153561819e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   7.5614440831194792e-06    7.5614440831194792e-06\n",
+      "Overall NLP error.......:   7.5614440831194792e-06    7.5614440831194792e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Reducing mu to   8.0738690075361328e-09 in fixed mu mode. Tau becomes   9.9999597541473606e-01\n",
+      "Barrier Parameter: 8.073869e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 2.477764e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 5.313539e-19\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 4 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 5.132967E-21\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.496e-17 (tol = 1.000e-07) r (rel) = 4.538e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  3.6706332367466481e-04  (reference  3.9014230695305662e-04):\n",
+      "  New values of constraint violation =  1.3071412442028363e-17  (reference  2.7761504846259694e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.049094e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 7.044544e-06 snrm = 7.731534e-01 ynrm = 1.227059e-05\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.178478e-05\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 5:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   5  2.2478754e-04 0.00e+00 5.15e-06  -8.1 2.05e-01    -  7.60e-01 1.00e+00f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 5 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 8.0738690075361328e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999597541473606e-01\n",
+      "\n",
+      "||curr_x||_inf   = 8.5030114676927715e-01\n",
+      "||curr_s||_inf   = 4.8795497546569574e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 2.2434664157648190e-02\n",
+      "||curr_z_L||_inf = 3.7125796608629522e-06\n",
+      "||curr_z_U||_inf = 6.8639255528706076e-06\n",
+      "||curr_v_L||_inf = 2.2434667381497742e-02\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 5:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   2.2478753838083446e-04    2.2478753838083446e-04\n",
+      "Dual infeasibility......:   5.1460032095092485e-06    5.1460032095092485e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.8255007743336357e-06    1.8255007743336357e-06\n",
+      "Overall NLP error.......:   5.1460032095092485e-06    5.1460032095092485e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Barrier Parameter: 8.073869e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 1.164261e-14\n",
+      "Factorization successful.\n",
+      "residual_ratio = 2.224279e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 5 <--\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.319532E-22\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.101e-17 (tol = 1.000e-07) r (rel) = 4.139e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  3.2590083054756015e-04  (reference  3.6706332367466481e-04):\n",
+      "  New values of constraint violation =  5.0613606730233462e-17  (reference  1.3071412442028363e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 1.503443e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.535142e-05 snrm = 1.864300e+00 ynrm = 9.818142e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 4.416895e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 6:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   6  1.8088949e-04 0.00e+00 1.19e-06  -8.1 1.50e-01    -  8.31e-01 1.00e+00f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 6 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 8.0738690075361328e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999597541473606e-01\n",
+      "\n",
+      "||curr_x||_inf   = 8.6633944240021488e-01\n",
+      "||curr_s||_inf   = 4.7222866367873221e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 5.7600186012461531e-03\n",
+      "||curr_z_L||_inf = 8.3111298984257371e-07\n",
+      "||curr_z_U||_inf = 1.8149395552694020e-06\n",
+      "||curr_v_L||_inf = 5.7600196653849398e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 6:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.8088949364957464e-04    1.8088949364957464e-04\n",
+      "Dual infeasibility......:   1.1916321970272016e-06    1.1916321970272016e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   3.1754896585744371e-07    3.1754896585744371e-07\n",
+      "Overall NLP error.......:   1.1916321970272016e-06    1.1916321970272016e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "Switching back to free mu mode.\n",
+      "The current filter has 2 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 1.996573e-01\n",
+      "Barrier parameter mu computed by oracle is 3.362577e-08\n",
+      "Barrier parameter mu after safeguards is 3.362577e-08\n",
+      "Barrier Parameter: 3.362577e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 7.617584e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 8.568955e-19\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 6 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 7.719655E-22\n",
+      "Starting checks for alpha (primal) = 9.26e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.562e-17 (tol = 1.000e-07) r (rel) = 4.605e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 9.259950e-01:\n",
+      "  New values of barrier function     =  7.6292270100572794e-04  (reference  7.8482763483398105e-04):\n",
+      "  New values of constraint violation =  1.2358549513619144e-16  (reference  5.0613606730233462e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 1.775743e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.263361e-05 snrm = 2.220251e+00 ynrm = 7.574498e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 2.562850e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 7:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   7  1.4490905e-04 0.00e+00 7.32e-07  -7.5 1.92e-01    -  9.94e-01 9.26e-01f  1  sigma=2.00e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 7 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 3.3625767094389832e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999880836780297e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999985072576414e-01\n",
+      "||curr_s||_inf   = 8.5632546004813691e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 2.0785282382648398e-03\n",
+      "||curr_z_L||_inf = 2.9317796180187508e-07\n",
+      "||curr_z_U||_inf = 2.2092410046839914e-06\n",
+      "||curr_v_L||_inf = 2.0785137940958461e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 7:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.4490904732650581e-04    1.4490904732650581e-04\n",
+      "Dual infeasibility......:   7.3218678773143602e-07    7.3218678773143602e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   8.2321848990866307e-08    8.2321848990866307e-08\n",
+      "Overall NLP error.......:   7.3218678773143602e-07    7.3218678773143602e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 3 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 2.810308e-01\n",
+      "Barrier parameter mu computed by oracle is 9.963785e-09\n",
+      "Barrier parameter mu after safeguards is 9.963785e-09\n",
+      "Barrier Parameter: 9.963785e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 7.649897e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 2.009418e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 7 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.873008E-22\n",
+      "Starting checks for alpha (primal) = 7.66e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.380e-17 (tol = 1.000e-07) r (rel) = 4.421e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 7.661435e-01:\n",
+      "  New values of barrier function     =  3.0029377200096221e-04  (reference  3.2803514044315652e-04):\n",
+      "  New values of constraint violation =  2.6688144695499044e-16  (reference  1.2358549513619144e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.018803e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.452601e-05 snrm = 3.181887e+00 ynrm = 6.264839e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.434752e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 8:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   8  1.0928798e-04 0.00e+00 5.60e-07  -8.0 3.94e-01    -  9.67e-01 7.66e-01f  1  sigma=2.81e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 8 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 9.9637852972779224e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999926781321224e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999996054858042e-01\n",
+      "||curr_s||_inf   = 9.4305136140833296e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 9.2694484903164042e-04\n",
+      "||curr_z_L||_inf = 1.3153394871418884e-07\n",
+      "||curr_z_U||_inf = 1.4159853410464294e-06\n",
+      "||curr_v_L||_inf = 9.2692327334428369e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 8:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.0928798121901817e-04    1.0928798121901817e-04\n",
+      "Dual infeasibility......:   5.5975515905211401e-07    5.5975515905211401e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   2.8083811107694010e-08    2.8083811107694010e-08\n",
+      "Overall NLP error.......:   5.5975515905211401e-07    5.5975515905211401e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 4 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 4.758978e-01\n",
+      "Barrier parameter mu computed by oracle is 5.700956e-09\n",
+      "Barrier parameter mu after safeguards is 5.700956e-09\n",
+      "Barrier Parameter: 5.700956e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 3.950477e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 6.605249e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 8 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.686997E-22\n",
+      "Starting checks for alpha (primal) = 7.54e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.047e-17 (tol = 1.000e-07) r (rel) = 5.095e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 7.537482e-01:\n",
+      "  New values of barrier function     =  2.0047483415567866e-04  (reference  2.1857532493634615e-04):\n",
+      "  New values of constraint violation =  2.1060966013709825e-16  (reference  2.6688144695499044e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.300635e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 9.816937e-06 snrm = 3.436755e+00 ynrm = 4.021257e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 8.311491e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 9:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   9  8.4853144e-05 0.00e+00 4.46e-07  -8.2 3.05e-01    -  9.83e-01 7.54e-01f  1  sigma=4.76e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 9 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 5.7009561021759740e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999944024484100e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999993017425948e-01\n",
+      "||curr_s||_inf   = 9.7087544320580980e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 5.7393491258124311e-04\n",
+      "||curr_z_L||_inf = 7.0909329602160347e-08\n",
+      "||curr_z_U||_inf = 1.3095410294672740e-06\n",
+      "||curr_v_L||_inf = 5.7392199694711939e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 9:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   8.4853143994421878e-05    8.4853143994421878e-05\n",
+      "Dual infeasibility......:   4.4566903092094602e-07    4.4566903092094602e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.1876709675485983e-08    1.1876709675485983e-08\n",
+      "Overall NLP error.......:   4.4566903092094602e-07    4.4566903092094602e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 4 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 5.152993e-01\n",
+      "Barrier parameter mu computed by oracle is 3.242706e-09\n",
+      "Barrier parameter mu after safeguards is 3.242706e-09\n",
+      "Barrier Parameter: 3.242706e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 1.239075e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 2.049236e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 9 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.768659E-22\n",
+      "Starting checks for alpha (primal) = 6.24e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.028e-17 (tol = 1.000e-07) r (rel) = 5.076e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 6.242119e-01:\n",
+      "  New values of barrier function     =  1.4169120735636748e-04  (reference  1.5061880487584128e-04):\n",
+      "  New values of constraint violation =  3.3427308230443709e-17  (reference  2.1060966013709825e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 1.872844e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 3.746276e-06 snrm = 2.760733e+00 ynrm = 2.003784e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 4.915311e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 10:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  10  7.2333817e-05 0.00e+00 1.68e-07  -8.5 3.00e-01    -  9.89e-01 6.24e-01f  1  sigma=5.15e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 10 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 3.2427059766437888e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999955433096910e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999997907889071e-01\n",
+      "||curr_s||_inf   = 9.1722090811651073e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 3.8649323354190133e-04\n",
+      "||curr_z_L||_inf = 4.2929528640782552e-08\n",
+      "||curr_z_U||_inf = 6.4516179650102083e-07\n",
+      "||curr_v_L||_inf = 3.8648319042624229e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 10:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   7.2333816527303332e-05    7.2333816527303332e-05\n",
+      "Dual infeasibility......:   1.6781998495180137e-07    1.6781998495180137e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   6.2116934612765409e-09    6.2116934612765409e-09\n",
+      "Overall NLP error.......:   1.6781998495180137e-07    1.6781998495180137e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 2 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 6.079627e-01\n",
+      "Barrier parameter mu computed by oracle is 2.170914e-09\n",
+      "Barrier parameter mu after safeguards is 2.170914e-09\n",
+      "Barrier Parameter: 2.170914e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 4.510048e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 4.136234e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 10 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.843753E-23\n",
+      "Starting checks for alpha (primal) = 3.53e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.848e-17 (tol = 1.000e-07) r (rel) = 4.893e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 3.527003e-01:\n",
+      "  New values of barrier function     =  1.1500400208612345e-04  (reference  1.1876693177720237e-04):\n",
+      "  New values of constraint violation =  1.1539976873392588e-17  (reference  3.3427308230443709e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 1.326686e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 8.227656e-07 snrm = 1.663479e+00 ynrm = 8.581268e-07\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 2.973320e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 11:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  11  6.7048961e-05 0.00e+00 1.43e-07  -8.7 3.76e-01    -  1.00e+00 3.53e-01f  1  sigma=6.08e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 11 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 2.1709141381359714e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999983218001509e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999999884920221e-01\n",
+      "||curr_s||_inf   = 8.6292423117755249e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 3.0123199378696483e-04\n",
+      "||curr_z_L||_inf = 4.1909700560931919e-08\n",
+      "||curr_z_U||_inf = 6.7417118202554377e-07\n",
+      "||curr_v_L||_inf = 3.0122252054007694e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 11:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   6.7048960853371178e-05    6.7048960853371178e-05\n",
+      "Dual infeasibility......:   1.4324731554481346e-07    1.4324731554481346e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   5.1315041643808989e-09    5.1315041643808989e-09\n",
+      "Overall NLP error.......:   1.4324731554481346e-07    1.4324731554481346e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 2 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 8.624418e+00\n",
+      "Barrier parameter mu computed by oracle is 2.120410e-08\n",
+      "Barrier parameter mu after safeguards is 2.120410e-08\n",
+      "Barrier Parameter: 2.120410e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 6.125418e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.191130e-16\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 11 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.679671E-26\n",
+      "Starting checks for alpha (primal) = 1.71e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.291e-17 (tol = 1.000e-07) r (rel) = 5.341e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.713067e-01:\n",
+      "  New values of barrier function     =  5.1949790423902139e-04  (reference  5.3544303240504938e-04):\n",
+      "  New values of constraint violation =  1.1614007552982614e-16  (reference  1.1539976873392588e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.830253e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.583843e-06 snrm = 2.549492e+00 ynrm = 1.360322e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 2.436715e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 12:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  12  7.5850625e-05 0.00e+00 3.46e-07  -7.7 2.24e+00    -  5.59e-01 1.71e-01f  1  sigma=8.62e+00 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 12 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 2.1204096295430305e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999985675268444e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9170758955133220e-01\n",
+      "||curr_s||_inf   = 1.3045157151553733e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.0028678654938740e-03\n",
+      "||curr_z_L||_inf = 1.1939250812824760e-07\n",
+      "||curr_z_U||_inf = 9.1269260435600649e-07\n",
+      "||curr_v_L||_inf = 1.0028822186672719e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 12:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   7.5850624839271036e-05    7.5850624839271036e-05\n",
+      "Dual infeasibility......:   3.4588449281436793e-07    3.4588449281436793e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.3955793184101916e-08    1.3955793184101916e-08\n",
+      "Overall NLP error.......:   3.4588449281436793e-07    3.4588449281436793e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "Switching to fixed mu mode with mu =   1.0234391828744293e-08 and tau =   9.9999998976560822e-01.\n",
+      "Barrier Parameter: 1.023439e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 1.018810e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 8.555378e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 12 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 9.422692E-25\n",
+      "Starting checks for alpha (primal) = 6.15e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.189e-17 (tol = 1.000e-07) r (rel) = 5.238e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 6.153251e-01:\n",
+      "  New values of barrier function     =  2.8910241919670053e-04  (reference  2.8998189588579787e-04):\n",
+      "  New values of constraint violation =  1.6919821934583551e-16  (reference  1.1614007552982614e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.032926e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.533249e-08 snrm = 1.134755e+00 ynrm = 3.087990e-07\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.190716e-08\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 13:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  13  7.6043466e-05 0.00e+00 1.15e-07  -8.0 3.30e-01    -  1.00e+00 6.15e-01f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 13 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.0234391828744293e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999998976560822e-01\n",
+      "\n",
+      "||curr_x||_inf   = 1.0000000086814673e+00\n",
+      "||curr_s||_inf   = 1.3346289688018320e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 7.4633856811076615e-04\n",
+      "||curr_z_L||_inf = 1.4238079548713705e-07\n",
+      "||curr_z_U||_inf = 8.9620574544999946e-07\n",
+      "||curr_v_L||_inf = 7.4634058395468936e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 13:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   7.6043465991924942e-05    7.6043465991924942e-05\n",
+      "Dual infeasibility......:   1.1512661096963838e-07    1.1512661096963838e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   2.8944963897623939e-08    2.8944963897623939e-08\n",
+      "Overall NLP error.......:   1.1512661096963838e-07    1.1512661096963838e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Barrier Parameter: 1.023439e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 1.769512e-14\n",
+      "Factorization successful.\n",
+      "residual_ratio = 4.796655e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 13 <--\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 8.459886E-26\n",
+      "Starting checks for alpha (primal) = 9.00e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.867e-17 (tol = 1.000e-07) r (rel) = 4.913e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 8.995352e-01:\n",
+      "  New values of barrier function     =  2.8817205501058079e-04  (reference  2.8910241919670053e-04):\n",
+      "  New values of constraint violation =  1.7701294531720368e-16  (reference  1.6919821934583551e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 4.781683e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.111931e-06 snrm = 4.565202e+00 ynrm = 1.234560e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 5.335290e-08\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 14:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  14  7.7168866e-05 0.00e+00 4.03e-07  -8.0 5.32e-01    -  9.27e-01 9.00e-01f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 14 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.0234391828744293e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999998976560822e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.8845041864171557e-01\n",
+      "||curr_s||_inf   = 1.4221032779714803e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 7.1441837810079566e-04\n",
+      "||curr_z_L||_inf = 8.4276522268695759e-08\n",
+      "||curr_z_U||_inf = 8.7313268274591788e-07\n",
+      "||curr_v_L||_inf = 7.1441846827555665e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 14:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   7.7168866459873133e-05    7.7168866459873133e-05\n",
+      "Dual infeasibility......:   4.0281593147673594e-07    4.0281593147673594e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.6218304342515872e-08    1.6218304342515872e-08\n",
+      "Overall NLP error.......:   4.0281593147673594e-07    4.0281593147673594e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Barrier Parameter: 1.023439e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 1.875747e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 3.126641e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 14 <--\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.911216E-25\n",
+      "Starting checks for alpha (primal) = 9.70e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.174e-17 (tol = 1.000e-07) r (rel) = 5.222e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 9.703047e-01:\n",
+      "  New values of barrier function     =  2.8772620146418591e-04  (reference  2.8817205501058079e-04):\n",
+      "  New values of constraint violation =  1.6224238478298320e-16  (reference  1.7701294531720368e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.931369e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 6.936403e-07 snrm = 1.195057e+00 ynrm = 8.699207e-07\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 4.856873e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 15:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  15  7.6969579e-05 0.00e+00 9.18e-08  -8.0 4.05e-01    -  1.00e+00 9.70e-01f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 15 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.0234391828744293e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999998976560822e-01\n",
+      "\n",
+      "||curr_x||_inf   = 1.0000000067049402e+00\n",
+      "||curr_s||_inf   = 1.4230289881562869e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 7.1868097674196397e-04\n",
+      "||curr_z_L||_inf = 8.5346878920991984e-08\n",
+      "||curr_z_U||_inf = 8.5956714608447506e-07\n",
+      "||curr_v_L||_inf = 7.1868121897604637e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 15:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   7.6969579449427095e-05    7.6969579449427095e-05\n",
+      "Dual infeasibility......:   9.1818739185944381e-08    9.1818739185944381e-08\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.3964297591438233e-08    1.3964297591438233e-08\n",
+      "Overall NLP error.......:   9.1818739185944381e-08    9.1818739185944381e-08\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Reducing mu to   9.0909090909090910e-10 in fixed mu mode. Tau becomes   9.9999998976560822e-01\n",
+      "Barrier Parameter: 9.090909e-10\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 4.028402e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 5.969527e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 15 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.066631E-23\n",
+      "Starting checks for alpha (primal) = 7.03e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.681e-17 (tol = 1.000e-07) r (rel) = 5.735e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 7.028143e-01:\n",
+      "  New values of barrier function     =  8.4821763779407538e-05  (reference  9.5690469983069498e-05):\n",
+      "  New values of constraint violation =  1.1052594115542463e-16  (reference  1.6224238478298320e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 7.126767e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 4.002777e-06 snrm = 3.824446e+00 ynrm = 2.041555e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 2.736682e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 16:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  16  6.4483124e-05 0.00e+00 1.61e-07  -9.0 1.01e+00    -  1.95e-01 7.03e-01f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 16 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 9.0909090909090910e-10\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999998976560822e-01\n",
+      "\n",
+      "||curr_x||_inf   = 1.0000000085928633e+00\n",
+      "||curr_s||_inf   = 1.0565045421518312e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 6.4217500291686666e-04\n",
+      "||curr_z_L||_inf = 7.7050588706666413e-08\n",
+      "||curr_z_U||_inf = 8.1160375126493416e-07\n",
+      "||curr_v_L||_inf = 6.4218310874448699e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 16:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   6.4483124310314279e-05    6.4483124310314279e-05\n",
+      "Dual infeasibility......:   1.6079367566407334e-07    1.6079367566407334e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.1137172810707333e-08    1.1137172810707333e-08\n",
+      "Overall NLP error.......:   1.6079367566407334e-07    1.6079367566407334e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "Switching back to free mu mode.\n",
+      "The current filter has 3 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 1.258987e-03\n",
+      "Barrier parameter mu computed by oracle is 1.000000e-11\n",
+      "Barrier parameter mu after safeguards is 1.000000e-11\n",
+      "Barrier Parameter: 1.000000e-11\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 6.211181e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.501329e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 16 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 4.499179E-21\n",
+      "Starting checks for alpha (primal) = 3.19e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.693e-17 (tol = 1.000e-07) r (rel) = 5.746e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 3.186528e-01:\n",
+      "  New values of barrier function     =  6.1269472500741982e-05  (reference  6.4706849344474309e-05):\n",
+      "  New values of constraint violation =  1.5930995671958881e-16  (reference  1.1052594115542463e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 17:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  17  6.1037878e-05 0.00e+00 1.08e-07 -11.0 6.50e-01    -  3.23e-01 3.19e-01f  1  sigma=1.26e-03 qf=13y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 17 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 9.9999999999999994e-12\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999983920632429e-01\n",
+      "\n",
+      "||curr_x||_inf   = 1.0000000024193492e+00\n",
+      "||curr_s||_inf   = 9.2805963816738961e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 5.1396775197256185e-04\n",
+      "||curr_z_L||_inf = 5.8495053494723876e-08\n",
+      "||curr_z_U||_inf = 7.4569014919371131e-07\n",
+      "||curr_v_L||_inf = 5.1397334794937920e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 17:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   6.1037878106871076e-05    6.1037878106871076e-05\n",
+      "Dual infeasibility......:   1.0777843626581685e-07    1.0777843626581685e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   7.4709130714281063e-09    7.4709130714281063e-09\n",
+      "Overall NLP error.......:   1.0777843626581685e-07    1.0777843626581685e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "Number of Iterations....: 17\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   6.1037878106871076e-05    6.1037878106871076e-05\n",
+      "Dual infeasibility......:   1.0777843626581685e-07    1.0777843626581685e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   7.4709130714281063e-09    7.4709130714281063e-09\n",
+      "Overall NLP error.......:   1.0777843626581685e-07    1.0777843626581685e-07\n",
+      "\n",
+      "\n",
+      "Number of objective function evaluations             = 18\n",
+      "Number of objective gradient evaluations             = 18\n",
+      "Number of equality constraint evaluations            = 0\n",
+      "Number of inequality constraint evaluations          = 18\n",
+      "Number of equality constraint Jacobian evaluations   = 0\n",
+      "Number of inequality constraint Jacobian evaluations = 18\n",
+      "Number of Lagrangian Hessian evaluations             = 0\n",
+      "Total CPU secs in IPOPT (w/o function evaluations)   =      1.921\n",
+      "Total CPU secs in NLP function evaluations           =      1.984\n",
+      "\n",
+      "EXIT: Solved To Acceptable Level.\n",
+      "v=0.3\n",
+      "w=0.5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "rm: cannot remove '/home/fenics/shared/templates/RES_OPT/TEMP*': No such file or directory\n",
+      "WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 2.744e-16 (tol = 1.000e-07) r (rel) = 2.769e-12 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "List of options:\n",
+      "\n",
+      "                                    Name   Value                # times used\n",
+      "                          acceptable_tol = 0.001                     1\n",
+      "                        file_print_level = 5                         1\n",
+      "                   hessian_approximation = limited-memory            7\n",
+      "                                max_iter = 100                       1\n",
+      "                             output_file = /home/fenics/shared/templates/RES_OPT/solution_V=0.4_w=0.7.txt      1\n",
+      "                             print_level = 6                         2\n",
+      "\n",
+      "******************************************************************************\n",
+      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
+      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
+      "         For more information visit http://projects.coin-or.org/Ipopt\n",
+      "******************************************************************************\n",
+      "\n",
+      "This is Ipopt version 3.12.9, running with linear solver mumps.\n",
+      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
+      "\n",
+      "Number of nonzeros in equality constraint Jacobian...:        0\n",
+      "Number of nonzeros in inequality constraint Jacobian.:    10201\n",
+      "Number of nonzeros in Lagrangian Hessian.............:        0\n",
+      "\n",
+      "Hessian approximation will be done in the space of all 10201 x variables.\n",
+      "\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Scaling parameter for objective function = 1.000000e+00\n",
+      "objective scaling factor = 1\n",
+      "No x scaling provided\n",
+      "No c scaling provided\n",
+      "No d scaling provided\n",
+      "Moved initial values of x sufficiently inside the bounds.\n",
+      "Initial values of s sufficiently inside the bounds.\n",
+      "MUMPS used permuting_scaling 5 and pivot_order 5.\n",
+      "           scaling will be 77.\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Least square estimates max(y_c) = 0.000000e+00, max(y_d) = 1.000851e+00\n",
+      "Total number of variables............................:    10201\n",
+      "                     variables with only lower bounds:        0\n",
+      "                variables with lower and upper bounds:    10201\n",
+      "                     variables with only upper bounds:        0\n",
+      "Total number of equality constraints.................:        0\n",
+      "Total number of inequality constraints...............:        1\n",
+      "        inequality constraints with only lower bounds:        1\n",
+      "   inequality constraints with lower and upper bounds:        0\n",
+      "        inequality constraints with only upper bounds:        0\n",
+      "\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.210e-17 (tol = 1.000e-07) r (rel) = 5.258e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "Limited-Memory approximation started; store data at current iterate.\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 0:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   0  6.0632598e-04 0.00e+00 3.64e-01   0.0 0.00e+00    -  0.00e+00 0.00e+00   0 y\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 0 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.0000000000000000e+00\n",
+      "Current fraction-to-the-boundary parameter tau = 0.0000000000000000e+00\n",
+      "\n",
+      "||curr_x||_inf   = 6.0563534498214722e-01\n",
+      "||curr_s||_inf   = 2.5885384523204169e-01\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.0008507486521290e+00\n",
+      "||curr_z_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_z_U||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 0:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   6.0632598111746430e-04    6.0632598111746430e-04\n",
+      "Dual infeasibility......:   3.6396912861514852e-01    3.6396912861514852e-01\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.9000001999999998e-01    9.9000001999999998e-01\n",
+      "Overall NLP error.......:   9.9000001999999998e-01    9.9000001999999998e-01\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "Setting mu_max to 4.999882e+02.\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 1 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 2.467321e-01\n",
+      "Barrier parameter mu computed by oracle is 1.233631e-01\n",
+      "Barrier parameter mu after safeguards is 1.233631e-01\n",
+      "Barrier Parameter: 1.233631e-01\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 5.175568e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 5.974682e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 0 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 0.000000E+00\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "trial_max is initialized to 1.000000e+04\n",
+      "trial_min is initialized to 1.000000e-04\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.832e-17 (tol = 1.000e-07) r (rel) = 4.876e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  2.4404794996445057e+03  (reference  4.4082351880547922e+03):\n",
+      "  New values of constraint violation =  1.2212453270876722e-15  (reference  0.0000000000000000e+00):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 1.232789e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.126891e+00 snrm = 1.004418e+01 ynrm = 9.364628e-01\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.117000e-02\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 1:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   1  5.7809828e-04 0.00e+00 2.40e-01  -0.9 1.23e-01    -  9.98e-01 1.00e+00f  1  sigma=2.47e-01 qf=12y \n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 1 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.2336314121436313e-01\n",
+      "Current fraction-to-the-boundary parameter tau = 9.8999999999999999e-01\n",
+      "\n",
+      "||curr_x||_inf   = 5.8458155162310477e-01\n",
+      "||curr_s||_inf   = 1.7242998671615944e-01\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 8.9607702691559810e-01\n",
+      "||curr_z_L||_inf = 3.6632804485714987e-01\n",
+      "||curr_z_U||_inf = 2.6388465773350467e-01\n",
+      "||curr_v_L||_inf = 8.1074697870877988e-01\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 1:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   5.7809828387403938e-04    5.7809828387403938e-04\n",
+      "Dual infeasibility......:   2.4041630124405344e-01    2.4041630124405344e-01\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   2.1696344889658170e-01    2.1696344889658170e-01\n",
+      "Overall NLP error.......:   2.4041630124405344e-01    2.4041630124405344e-01\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 2 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 1.000000e-06\n",
+      "Barrier parameter mu computed by oracle is 1.288476e-07\n",
+      "Barrier parameter mu after safeguards is 1.288476e-07\n",
+      "Barrier Parameter: 1.288476e-07\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 5.505580e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 9.768880e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 1 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 3.678597E-17\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.074e-17 (tol = 1.000e-07) r (rel) = 5.120e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  3.1270583892432208e-03  (reference  3.1270749789700228e-03):\n",
+      "  New values of constraint violation =  5.5511151231257827e-17  (reference  1.2212453270876722e-15):\n",
+      "Checking sufficient reduction...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 4.540443e-05\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.649985e-12 snrm = 1.231477e-04 ynrm = 1.939551e-08\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.087996e-04\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 2:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   2  5.7808343e-04 0.00e+00 2.42e-03  -6.9 4.54e-05    -  9.90e-01 1.00e+00h  1 Nhj  sigma=1.00e-06 qf=13y \n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 2 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.2884756725947034e-07\n",
+      "Current fraction-to-the-boundary parameter tau = 9.8999999999999999e-01\n",
+      "\n",
+      "||curr_x||_inf   = 5.8458272581402138e-01\n",
+      "||curr_s||_inf   = 1.7242934515766895e-01\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 9.0372120362592762e-03\n",
+      "||curr_z_L||_inf = 3.6932230748261685e-03\n",
+      "||curr_z_U||_inf = 2.6612188709363438e-03\n",
+      "||curr_v_L||_inf = 8.1769808436709113e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 2:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   5.7808342830889677e-04    5.7808342830889677e-04\n",
+      "Dual infeasibility......:   2.4228214744788160e-03    2.4228214744788160e-03\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   2.1873527015684241e-03    2.1873527015684241e-03\n",
+      "Overall NLP error.......:   2.4228214744788160e-03    2.4228214744788160e-03\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "Switching to fixed mu mode with mu =   1.0392368597111350e-03 and tau =   9.9896076314028881e-01.\n",
+      "Barrier Parameter: 1.039237e-03\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 2.723651e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 3.158429e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 2 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.673306E-27\n",
+      "Starting checks for alpha (primal) = 7.59e-02\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.125e-17 (tol = 1.000e-07) r (rel) = 5.172e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 7.585769e-02:\n",
+      "  New values of barrier function     =  1.9832350390496664e+01  (reference  2.0559668101396795e+01):\n",
+      "  New values of constraint violation =  8.3266726846886741e-17  (reference  5.5511151231257827e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 1.331403e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = -1.451859e-06 snrm = 1.439043e+00 ynrm = 2.731417e-06\n",
+      "     Skip the update.\n",
+      "Number of successive iterations with skipping: 1\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 3:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   3  5.6956893e-04 0.00e+00 3.64e-03  -3.0 1.76e+00    -  3.92e-01 7.59e-02f  1 Fy Ws\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 3 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.0392368597111350e-03\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9896076314028881e-01\n",
+      "\n",
+      "||curr_x||_inf   = 5.7941527882232635e-01\n",
+      "||curr_s||_inf   = 1.6002931706506521e-01\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.0907352273538005e-02\n",
+      "||curr_z_L||_inf = 4.6100499956645852e-03\n",
+      "||curr_z_U||_inf = 2.4612646723549936e-03\n",
+      "||curr_v_L||_inf = 1.0371590958951080e-02\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 3:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   5.6956892860383251e-04    5.6956892860383251e-04\n",
+      "Dual infeasibility......:   3.6380072938206696e-03    3.6380072938206696e-03\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.8993069172678625e-03    1.8993069172678625e-03\n",
+      "Overall NLP error.......:   3.6380072938206696e-03    3.6380072938206696e-03\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "Switching back to free mu mode.\n",
+      "The current filter has 2 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 8.050304e-03\n",
+      "Barrier parameter mu computed by oracle is 9.380861e-06\n",
+      "Barrier parameter mu after safeguards is 9.380861e-06\n",
+      "Barrier Parameter: 9.380861e-06\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 1.253733e-14\n",
+      "Factorization successful.\n",
+      "residual_ratio = 6.034818e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 3 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 4.029331E-23\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.084e-17 (tol = 1.000e-07) r (rel) = 5.131e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  1.7870549619355253e-01  (reference  1.7958476082294542e-01):\n",
+      "  New values of constraint violation =  2.7755575615628914e-17  (reference  8.3266726846886741e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 4.857406e-03\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = -4.301490e-09 snrm = 1.990570e-01 ynrm = 2.238642e-06\n",
+      "     Skip the update.\n",
+      "Number of successive iterations with skipping: 2\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 4:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   4  5.6668739e-04 0.00e+00 2.77e-04  -5.0 4.86e-03    -  9.95e-01 1.00e+00f  1  sigma=8.05e-03 qf=13y AWs\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 4 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 9.3808613325604514e-06\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9636199270617931e-01\n",
+      "\n",
+      "||curr_x||_inf   = 5.7893703636065474e-01\n",
+      "||curr_s||_inf   = 1.5826856453898955e-01\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 2.2516872520260452e-04\n",
+      "||curr_z_L||_inf = 2.8671434170452186e-04\n",
+      "||curr_z_U||_inf = 5.8751362109901641e-05\n",
+      "||curr_v_L||_inf = 2.2195881008565954e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 4:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   5.6668738821619049e-04    5.6668738821619049e-04\n",
+      "Dual infeasibility......:   2.7734828196926051e-04    2.7734828196926051e-04\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   3.5129104478627629e-05    3.5129104478627629e-05\n",
+      "Overall NLP error.......:   2.7734828196926051e-04    2.7734828196926051e-04\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 2 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 1.363906e-01\n",
+      "Barrier parameter mu computed by oracle is 1.960047e-06\n",
+      "Barrier parameter mu after safeguards is 1.960047e-06\n",
+      "Barrier Parameter: 1.960047e-06\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 6.279930e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 2.166223e-16\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 4 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 4.082368E-24\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.468e-17 (tol = 1.000e-07) r (rel) = 4.509e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  3.4903048616193505e-02  (reference  3.7787202145653939e-02):\n",
+      "  New values of constraint violation =  2.2204460492503131e-16  (reference  2.7755575615628914e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "Resetting Limited-Memory Update.\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 5:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   5  4.7462820e-04 0.00e+00 1.27e-04  -5.7 1.28e-01    -  9.93e-01 1.00e+00f  1  sigma=1.36e-01 qf=12y AWr\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 5 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.9600472800264057e-06\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9972265171803076e-01\n",
+      "\n",
+      "||curr_x||_inf   = 6.7970699058116035e-01\n",
+      "||curr_s||_inf   = 1.2360980340370592e-01\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 6.2567581658318962e-05\n",
+      "||curr_z_L||_inf = 1.2891044391489718e-04\n",
+      "||curr_z_U||_inf = 2.1410950720915148e-05\n",
+      "||curr_v_L||_inf = 6.2171589741291498e-05\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 5:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   4.7462820104574017e-04    4.7462820104574017e-04\n",
+      "Dual infeasibility......:   1.2684663439650783e-04    1.2684663439650783e-04\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   7.6850186069327993e-06    7.6850186069327993e-06\n",
+      "Overall NLP error.......:   1.2684663439650783e-04    1.2684663439650783e-04\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 3 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 4.997848e-06\n",
+      "Barrier parameter mu computed by oracle is 1.000000e-11\n",
+      "Barrier parameter mu after safeguards is 1.000000e-11\n",
+      "Barrier Parameter: 1.000000e-11\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 2.272951e-12\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.324751e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 5 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.534259E-17\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.538e-17 (tol = 1.000e-07) r (rel) = 4.580e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  4.7479661872267052e-04  (reference  4.7480385201480597e-04):\n",
+      "  New values of constraint violation =  2.7755575615628914e-17  (reference  2.2204460492503131e-16):\n",
+      "Checking sufficient reduction...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.182327e-05\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 2.466370e-13 snrm = 8.506497e-05 ynrm = 4.248028e-09\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 3.408448e-05\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 6:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   6  4.7462097e-04 0.00e+00 2.18e-05 -11.0 2.18e-05    -  1.00e+00 1.00e+00h  1  sigma=5.00e-06 qf=13y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 6 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 9.9999999999999994e-12\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9987315336560345e-01\n",
+      "\n",
+      "||curr_x||_inf   = 6.7972881385126871e-01\n",
+      "||curr_s||_inf   = 1.2360938376226158e-01\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 4.2923113340762616e-07\n",
+      "||curr_z_L||_inf = 2.0347424935544204e-08\n",
+      "||curr_z_U||_inf = 4.7097993893681345e-09\n",
+      "||curr_v_L||_inf = 9.6418110211377375e-09\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 6:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   4.7462096777451974e-04    4.7462096777451974e-04\n",
+      "Dual infeasibility......:   2.1817850862022120e-05    2.1817850862022120e-05\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.5084130840534965e-09    1.5084130840534965e-09\n",
+      "Overall NLP error.......:   2.1817850862022120e-05    2.1817850862022120e-05\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 2 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20726\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 245484\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 1.804931e+00\n",
+      "Barrier parameter mu computed by oracle is 5.617646e-10\n",
+      "Barrier parameter mu after safeguards is 5.617646e-10\n",
+      "Barrier Parameter: 5.617646e-10\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 2.993105e-17\n",
+      "Factorization successful.\n",
+      "residual_ratio = 9.977016e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 6 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 3.044534E-23\n",
+      "Starting checks for alpha (primal) = 5.51e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.652e-17 (tol = 1.000e-07) r (rel) = 4.695e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 5.510821e-01:\n",
+      "  New values of barrier function     =  3.0578888845124653e-04  (reference  4.8448841639266702e-04):\n",
+      "  New values of constraint violation =  2.2204460492503131e-16  (reference  2.7755575615628914e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.832589e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.291217e-04 snrm = 3.299308e+00 ynrm = 4.959430e-05\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.186188e-05\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 7:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   7  2.9594486e-04 0.00e+00 4.13e-05  -9.3 6.95e-01    -  1.00e+00 5.51e-01f  1  sigma=1.80e+00 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 7 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 5.6176460884667023e-10\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9997818214913803e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999164793212114e-01\n",
+      "||curr_s||_inf   = 1.0557144843963795e-01\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 2.5119488663727922e-07\n",
+      "||curr_z_L||_inf = 5.9637636961660206e-08\n",
+      "||curr_z_U||_inf = 5.4688907266590252e-09\n",
+      "||curr_v_L||_inf = 7.0978335094288167e-09\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 7:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   2.9594486325079872e-04    2.9594486325079872e-04\n",
+      "Dual infeasibility......:   4.1319838059223542e-05    4.1319838059223542e-05\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   8.9169472495952544e-10    8.9169472495952544e-10\n",
+      "Overall NLP error.......:   4.1319838059223542e-05    4.1319838059223542e-05\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 3 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20726\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 245484\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 3.912269e+00\n",
+      "Barrier parameter mu computed by oracle is 2.211469e-09\n",
+      "Barrier parameter mu after safeguards is 2.211469e-09\n",
+      "Barrier Parameter: 2.211469e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 5.384779e-17\n",
+      "Factorization successful.\n",
+      "residual_ratio = 3.167517e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 7 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.132093E-22\n",
+      "Starting checks for alpha (primal) = 1.62e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.444e-17 (tol = 1.000e-07) r (rel) = 4.485e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.623140e-01:\n",
+      "  New values of barrier function     =  2.8367530917845736e-04  (reference  3.3469730900024359e-04):\n",
+      "  New values of constraint violation =  9.7144514654701197e-17  (reference  2.2204460492503131e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.555651e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 2.754160e-05 snrm = 1.245135e+00 ynrm = 4.810689e-05\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.776464e-05\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 8:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   8  2.4494997e-04 0.00e+00 9.13e-06  -8.7 2.19e+00    -  1.00e+00 1.62e-01f  1  sigma=3.91e+00 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 8 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 2.2114685898303495e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9995868016194078e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9998531749985653e-01\n",
+      "||curr_s||_inf   = 9.8911705019217769e-02\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 2.2521759652297025e-07\n",
+      "||curr_z_L||_inf = 1.2557820156188865e-07\n",
+      "||curr_z_U||_inf = 6.4965559832340876e-06\n",
+      "||curr_v_L||_inf = 2.3706148404701627e-08\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 8:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   2.4494997476961729e-04    2.4494997476961729e-04\n",
+      "Dual infeasibility......:   9.1259992116974073e-06    9.1259992116974073e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   4.1598322979235200e-07    4.1598322979235200e-07\n",
+      "Overall NLP error.......:   9.1259992116974073e-06    9.1259992116974073e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 3 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20726\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 245484\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 5.247048e-01\n",
+      "Barrier parameter mu computed by oracle is 1.168871e-09\n",
+      "Barrier parameter mu after safeguards is 1.168871e-09\n",
+      "Barrier Parameter: 1.168871e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 1.205134e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.854053e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 8 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 3.189261E-22\n",
+      "Starting checks for alpha (primal) = 4.43e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.681e-17 (tol = 1.000e-07) r (rel) = 4.724e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 4.434101e-01:\n",
+      "  New values of barrier function     =  2.1015847292720386e-04  (reference  2.6541824168166081e-04):\n",
+      "  New values of constraint violation =  0.0000000000000000e+00  (reference  9.7144514654701197e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 1.659446e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.746430e-05 snrm = 1.964375e+00 ynrm = 1.020056e-05\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 4.525873e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 9:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   9  1.8967944e-04 0.00e+00 8.80e-06  -8.9 3.74e-01    -  1.00e+00 4.43e-01f  1  sigma=5.25e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 9 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.1688712326242752e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999087400078834e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999911421011822e-01\n",
+      "||curr_s||_inf   = 8.7229631548235875e-02\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.6510359940956756e-07\n",
+      "||curr_z_L||_inf = 1.6977757737158464e-08\n",
+      "||curr_z_U||_inf = 1.4770869313922699e-05\n",
+      "||curr_v_L||_inf = 1.8131653123302750e-08\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 9:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.8967943962008491e-04    1.8967943962008491e-04\n",
+      "Dual infeasibility......:   8.8010660757903920e-06    8.8010660757903920e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   5.5581777801334431e-07    5.5581777801334431e-07\n",
+      "Overall NLP error.......:   8.8010660757903920e-06    8.8010660757903920e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 1 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20726\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 245484\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 5.712150e+00\n",
+      "Barrier parameter mu computed by oracle is 6.804777e-09\n",
+      "Barrier parameter mu after safeguards is 6.804777e-09\n",
+      "Barrier Parameter: 6.804777e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 2.611364e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.684751e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 9 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 0.000000E+00\n",
+      "Starting checks for alpha (primal) = 1.19e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.512e-17 (tol = 1.000e-07) r (rel) = 4.554e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.185538e-01:\n",
+      "  New values of barrier function     =  2.9359625148661368e-04  (reference  3.0890151339916074e-04):\n",
+      "  New values of constraint violation =  2.4980018054066022e-16  (reference  0.0000000000000000e+00):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 9.765522e-02\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.432240e-06 snrm = 6.891064e-01 ynrm = 2.845964e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 3.016083e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 10:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  10  1.7450685e-04 0.00e+00 4.72e-06  -8.2 8.24e-01    -  7.85e-01 1.19e-01f  1  sigma=5.71e+00 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 10 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 6.8047768781023318e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999119893392419e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999915052243504e-01\n",
+      "||curr_s||_inf   = 8.2537523747637370e-02\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 2.1705017790600958e-07\n",
+      "||curr_z_L||_inf = 2.7565271587902688e-07\n",
+      "||curr_z_U||_inf = 3.3651070777012089e-06\n",
+      "||curr_v_L||_inf = 7.1565545332452713e-08\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 10:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.7450684775751929e-04    1.7450684775751929e-04\n",
+      "Dual infeasibility......:   4.7168964129096463e-06    4.7168964129096463e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   8.1545100546006939e-08    8.1545100546006939e-08\n",
+      "Overall NLP error.......:   4.7168964129096463e-06    4.7168964129096463e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 2 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20726\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 245484\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 3.912269e+00\n",
+      "Barrier parameter mu computed by oracle is 2.192765e-08\n",
+      "Barrier parameter mu after safeguards is 2.192765e-08\n",
+      "Barrier Parameter: 2.192765e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 1.225039e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 3.546803e-20\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 10 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 3.325934E-23\n",
+      "Starting checks for alpha (primal) = 9.25e-02\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.607e-17 (tol = 1.000e-07) r (rel) = 4.649e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 9.245990e-02:\n",
+      "  New values of barrier function     =  5.4327826510422558e-04  (reference  5.5825944844870121e-04):\n",
+      "  New values of constraint violation =  1.3877787807814457e-17  (reference  2.4980018054066022e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 8.169888e-02\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.040866e-06 snrm = 7.519364e-01 ynrm = 2.004767e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.840909e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 11:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  11  1.6143144e-04 0.00e+00 3.99e-06  -7.7 8.84e-01    -  8.08e-01 9.25e-02f  1  sigma=3.91e+00 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 11 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 2.1927650507305542e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999528310358710e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999962463304148e-01\n",
+      "||curr_s||_inf   = 7.6238407362348146e-02\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 4.3545105117044743e-07\n",
+      "||curr_z_L||_inf = 2.0418149796009956e-07\n",
+      "||curr_z_U||_inf = 6.0675644200933568e-06\n",
+      "||curr_v_L||_inf = 2.7601256903364457e-07\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 11:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.6143143653598525e-04    1.6143143653598525e-04\n",
+      "Dual infeasibility......:   3.9925007150679478e-06    3.9925007150679478e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   7.8857175341615126e-08    7.8857175341615126e-08\n",
+      "Overall NLP error.......:   3.9925007150679478e-06    3.9925007150679478e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 2 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 5.214675e+00\n",
+      "Barrier parameter mu computed by oracle is 9.718882e-08\n",
+      "Barrier parameter mu after safeguards is 9.718882e-08\n",
+      "Barrier Parameter: 9.718882e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 4.588268e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 6.951921e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 11 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.425166E-25\n",
+      "Starting checks for alpha (primal) = 7.16e-02\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.695e-17 (tol = 1.000e-07) r (rel) = 4.738e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 7.158646e-02:\n",
+      "  New values of barrier function     =  1.8031951802543412e-03  (reference  1.8538718649524723e-03):\n",
+      "  New values of constraint violation =  1.3877787807814457e-16  (reference  1.3877787807814457e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.076230e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.349134e-06 snrm = 1.642182e+00 ynrm = 3.031010e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 5.002792e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 12:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  12  1.4616727e-04 0.00e+00 3.90e-06  -7.0 4.30e+00    -  7.21e-01 7.16e-02f  1  sigma=5.21e+00 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 12 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 9.7188818767730056e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999600749928497e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999943416939696e-01\n",
+      "||curr_s||_inf   = 6.1090664794212685e-02\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.7614114400852782e-06\n",
+      "||curr_z_L||_inf = 1.5838416428457125e-06\n",
+      "||curr_z_U||_inf = 4.9896288688892115e-06\n",
+      "||curr_v_L||_inf = 1.5481951742530442e-06\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 12:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.4616726848487558e-04    1.4616726848487558e-04\n",
+      "Dual infeasibility......:   3.8967095020415286e-06    3.8967095020415286e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.6049327131365953e-06    1.6049327131365953e-06\n",
+      "Overall NLP error.......:   3.8967095020415286e-06    3.8967095020415286e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 3 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 8.441891e-02\n",
+      "Barrier parameter mu computed by oracle is 6.158000e-09\n",
+      "Barrier parameter mu after safeguards is 6.158000e-09\n",
+      "Barrier Parameter: 6.158000e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 1.215735e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 2.805542e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 12 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 5.450510E-22\n",
+      "Starting checks for alpha (primal) = 7.17e-02\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.932e-17 (tol = 1.000e-07) r (rel) = 4.978e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 7.169744e-02:\n",
+      "  New values of barrier function     =  2.4445944661744868e-04  (reference  2.5115853512086507e-04):\n",
+      "  New values of constraint violation =  1.8735013540549517e-16  (reference  1.3877787807814457e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 1.595949e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 3.400575e-07 snrm = 5.031567e-01 ynrm = 1.740702e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.343216e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 13:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  13  1.3978413e-04 0.00e+00 3.62e-06  -8.2 2.23e+00    -  2.48e-01 7.17e-02f  1  sigma=8.44e-02 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 13 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 6.1579995803349112e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999610329049793e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999978536756351e-01\n",
+      "||curr_s||_inf   = 5.6981685989546761e-02\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.7174317408092741e-06\n",
+      "||curr_z_L||_inf = 1.1315752844938043e-06\n",
+      "||curr_z_U||_inf = 4.2503725426134735e-06\n",
+      "||curr_v_L||_inf = 1.5494338772653073e-06\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 13:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.3978413433019787e-04    1.3978413433019787e-04\n",
+      "Dual infeasibility......:   3.6183773174209675e-06    3.6183773174209675e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.2003700881234475e-06    1.2003700881234475e-06\n",
+      "Overall NLP error.......:   3.6183773174209675e-06    3.6183773174209675e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 4 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 4.231797e-02\n",
+      "Barrier parameter mu computed by oracle is 2.366330e-09\n",
+      "Barrier parameter mu after safeguards is 2.366330e-09\n",
+      "Barrier Parameter: 2.366330e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 1.065504e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 8.879202e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 13 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 9.939801E-22\n",
+      "Starting checks for alpha (primal) = 1.42e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.689e-17 (tol = 1.000e-07) r (rel) = 4.732e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.423306e-01:\n",
+      "  New values of barrier function     =  1.6887198362840910e-04  (reference  1.8000763296455044e-04):\n",
+      "  New values of constraint violation =  1.9428902930940239e-16  (reference  1.8735013540549517e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 1.112276e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 8.986321e-07 snrm = 8.534028e-01 ynrm = 1.713959e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.233882e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 14:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  14  1.2878289e-04 0.00e+00 3.25e-06  -8.6 7.81e-01    -  4.64e-01 1.42e-01f  1  sigma=4.23e-02 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 14 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 2.3663295795116899e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999638162268256e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999984354886429e-01\n",
+      "||curr_s||_inf   = 5.0117248554114840e-02\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.5703584637908501e-06\n",
+      "||curr_z_L||_inf = 6.3548105205339079e-07\n",
+      "||curr_z_U||_inf = 2.4740214478608344e-06\n",
+      "||curr_v_L||_inf = 1.4582507733731510e-06\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 14:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.2878289280940380e-04    1.2878289280940380e-04\n",
+      "Dual infeasibility......:   3.2522524724972614e-06    3.2522524724972614e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.0215871969018548e-07    1.0215871969018548e-07\n",
+      "Overall NLP error.......:   3.2522524724972614e-06    3.2522524724972614e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 5 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 3.492338e-01\n",
+      "Barrier parameter mu computed by oracle is 1.075817e-08\n",
+      "Barrier parameter mu after safeguards is 1.075817e-08\n",
+      "Barrier Parameter: 1.075817e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 6.836371e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 3.418186e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 14 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.475955E-23\n",
+      "Starting checks for alpha (primal) = 2.49e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.783e-17 (tol = 1.000e-07) r (rel) = 4.827e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 2.494349e-01:\n",
+      "  New values of barrier function     =  2.8793783012781349e-04  (reference  3.1104203556945210e-04):\n",
+      "  New values of constraint violation =  3.0184188481996443e-16  (reference  1.9428902930940239e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.038099e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 3.344709e-06 snrm = 2.279230e+00 ynrm = 2.807600e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 6.438459e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 15:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  15  1.0883023e-04 0.00e+00 2.47e-06  -8.0 1.22e+00    -  7.54e-01 2.49e-01f  1  sigma=3.49e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 15 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.0758168664805035e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999674774752745e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999998467105289e-01\n",
+      "||curr_s||_inf   = 2.9807991107479035e-02\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 2.3736033723722621e-06\n",
+      "||curr_z_L||_inf = 6.3329327364711866e-07\n",
+      "||curr_z_U||_inf = 3.5605240166691881e-06\n",
+      "||curr_v_L||_inf = 2.3073659570188645e-06\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 15:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.0883022957462301e-04    1.0883022957462301e-04\n",
+      "Dual infeasibility......:   2.4743478651117703e-06    2.4743478651117703e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.4375112849242822e-08    9.4375112849242822e-08\n",
+      "Overall NLP error.......:   2.4743478651117703e-06    2.4743478651117703e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 6 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 2.302406e+00\n",
+      "Barrier parameter mu computed by oracle is 3.508240e-08\n",
+      "Barrier parameter mu after safeguards is 3.508240e-08\n",
+      "Barrier Parameter: 3.508240e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 3.028676e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 6.730391e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 15 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 6.554749E-24\n",
+      "Starting checks for alpha (primal) = 1.17e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.171e-17 (tol = 1.000e-07) r (rel) = 5.218e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.167047e-01:\n",
+      "  New values of barrier function     =  6.6187976896193285e-04  (reference  6.9290023535316641e-04):\n",
+      "  New values of constraint violation =  4.2674197509029455e-16  (reference  3.0184188481996443e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.210703e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.182157e-06 snrm = 3.119149e+00 ynrm = 1.878009e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.215074e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 16:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  16  9.6549110e-05 0.00e+00 2.75e-06  -7.5 2.75e+00    -  6.41e-01 1.17e-01f  1  sigma=2.30e+00 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 16 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 3.5082395246276397e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999752565213484e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9808476905569421e-01\n",
+      "||curr_s||_inf   = 2.2102572293494945e-03\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.3389972551909327e-05\n",
+      "||curr_z_L||_inf = 9.9672398206692459e-07\n",
+      "||curr_z_U||_inf = 5.0638828746768668e-06\n",
+      "||curr_v_L||_inf = 1.3324379447899140e-05\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 16:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   9.6549110124383286e-05    9.6549110124383286e-05\n",
+      "Dual infeasibility......:   2.7478596920982142e-06    2.7478596920982142e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.7686422459341640e-08    9.7686422459341640e-08\n",
+      "Overall NLP error.......:   2.7478596920982142e-06    2.7478596920982142e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 7 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 1.804931e+00\n",
+      "Barrier parameter mu computed by oracle is 5.079196e-08\n",
+      "Barrier parameter mu after safeguards is 5.079196e-08\n",
+      "Barrier Parameter: 5.079196e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 7.247913e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.782274e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 16 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 6.256062E-23\n",
+      "Starting checks for alpha (primal) = 1.95e-02\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.812e-17 (tol = 1.000e-07) r (rel) = 4.857e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.946207e-02:\n",
+      "  New values of barrier function     =  9.1232498543689544e-04  (reference  9.1502943778027637e-04):\n",
+      "  New values of constraint violation =  4.2023676205538152e-16  (reference  4.2674197509029455e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 17:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 4.546274e-02\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = -3.574878e-10 snrm = 2.805333e-01 ynrm = 1.187297e-07\n",
+      "     Skip the update.\n",
+      "Number of successive iterations with skipping: 1\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 17:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  17  9.5888193e-05 0.00e+00 1.23e-06  -7.3 2.34e+00    -  5.27e-01 1.95e-02f  1  sigma=1.80e+00 qf=12y AWs\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 17 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 5.0791956719929144e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999725214030788e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9788881953043662e-01\n",
+      "||curr_s||_inf   = 3.9264957713176785e-09\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 3.7944528595895260e-04\n",
+      "||curr_z_L||_inf = 5.1911720873724571e-07\n",
+      "||curr_z_U||_inf = 3.5214221254470305e-06\n",
+      "||curr_v_L||_inf = 3.7940917796523173e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 17:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   9.5888192904728474e-05    9.5888192904728474e-05\n",
+      "Dual infeasibility......:   1.2309777274379566e-06    1.2309777274379566e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.4718469015240673e-08    9.4718469015240673e-08\n",
+      "Overall NLP error.......:   1.2309777274379566e-06    1.2309777274379566e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 17:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 7 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 1.327510e-02\n",
+      "Barrier parameter mu computed by oracle is 5.218537e-10\n",
+      "Barrier parameter mu after safeguards is 5.218537e-10\n",
+      "Barrier Parameter: 5.218537e-10\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 17:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 4.788278e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 7.365046e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 17:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 17 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 6.056113E-21\n",
+      "Starting checks for alpha (primal) = 5.69e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.639e-17 (tol = 1.000e-07) r (rel) = 4.682e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 5.686417e-01:\n",
+      "  New values of barrier function     =  8.7972490615553708e-05  (reference  1.0427653916588002e-04):\n",
+      "  New values of constraint violation =  2.0148986815224802e-16  (reference  4.2023676205538152e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 18:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  18  7.9311599e-05 0.00e+00 1.21e-06  -9.3 1.46e+00    -  6.55e-01 5.69e-01f  1  sigma=1.33e-02 qf=13y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 18 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 5.2185365008694458e-10\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999876902227258e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999898911190011e-01\n",
+      "||curr_s||_inf   = 7.7498913212568736e-07\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 3.6230944817811610e-04\n",
+      "||curr_z_L||_inf = 8.9612693988201973e-07\n",
+      "||curr_z_U||_inf = 2.0765106055154655e-06\n",
+      "||curr_v_L||_inf = 3.6229113813024297e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 18:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   7.9311598799361929e-05    7.9311598799361929e-05\n",
+      "Dual infeasibility......:   1.2133862307492080e-06    1.2133862307492080e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   4.4696644788203451e-08    4.4696644788203451e-08\n",
+      "Overall NLP error.......:   1.2133862307492080e-06    1.2133862307492080e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "Number of Iterations....: 18\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   7.9311598799361929e-05    7.9311598799361929e-05\n",
+      "Dual infeasibility......:   1.2133862307492080e-06    1.2133862307492080e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   4.4696644788203451e-08    4.4696644788203451e-08\n",
+      "Overall NLP error.......:   1.2133862307492080e-06    1.2133862307492080e-06\n",
+      "\n",
+      "\n",
+      "Number of objective function evaluations             = 19\n",
+      "Number of objective gradient evaluations             = 19\n",
+      "Number of equality constraint evaluations            = 0\n",
+      "Number of inequality constraint evaluations          = 19\n",
+      "Number of equality constraint Jacobian evaluations   = 0\n",
+      "Number of inequality constraint Jacobian evaluations = 19\n",
+      "Number of Lagrangian Hessian evaluations             = 0\n",
+      "Total CPU secs in IPOPT (w/o function evaluations)   =      2.118\n",
+      "Total CPU secs in NLP function evaluations           =      2.086\n",
+      "\n",
+      "EXIT: Solved To Acceptable Level.\n",
+      "v=0.4\n",
+      "w=0.7\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "rm: cannot remove '/home/fenics/shared/templates/RES_OPT/TEMP*': No such file or directory\n",
+      "WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 9.149e-17 (tol = 1.000e-07) r (rel) = 9.240e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "List of options:\n",
+      "\n",
+      "                                    Name   Value                # times used\n",
+      "                          acceptable_tol = 0.001                     1\n",
+      "                        file_print_level = 5                         1\n",
+      "                   hessian_approximation = limited-memory            7\n",
+      "                                max_iter = 100                       1\n",
+      "                             output_file = /home/fenics/shared/templates/RES_OPT/solution_V=0.2_w=0.1.txt      1\n",
+      "                             print_level = 6                         2\n",
+      "\n",
+      "******************************************************************************\n",
+      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
+      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
+      "         For more information visit http://projects.coin-or.org/Ipopt\n",
+      "******************************************************************************\n",
+      "\n",
+      "This is Ipopt version 3.12.9, running with linear solver mumps.\n",
+      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
+      "\n",
+      "Number of nonzeros in equality constraint Jacobian...:        0\n",
+      "Number of nonzeros in inequality constraint Jacobian.:    10201\n",
+      "Number of nonzeros in Lagrangian Hessian.............:        0\n",
+      "\n",
+      "Hessian approximation will be done in the space of all 10201 x variables.\n",
+      "\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Scaling parameter for objective function = 1.000000e+00\n",
+      "objective scaling factor = 1\n",
+      "No x scaling provided\n",
+      "No c scaling provided\n",
+      "No d scaling provided\n",
+      "Moved initial values of x sufficiently inside the bounds.\n",
+      "Initial values of s sufficiently inside the bounds.\n",
+      "MUMPS used permuting_scaling 5 and pivot_order 5.\n",
+      "           scaling will be 77.\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Least square estimates max(y_c) = 0.000000e+00, max(y_d) = 9.999494e-01\n",
+      "Total number of variables............................:    10201\n",
+      "                     variables with only lower bounds:        0\n",
+      "                variables with lower and upper bounds:    10201\n",
+      "                     variables with only upper bounds:        0\n",
+      "Total number of equality constraints.................:        0\n",
+      "Total number of inequality constraints...............:        1\n",
+      "        inequality constraints with only lower bounds:        1\n",
+      "   inequality constraints with lower and upper bounds:        0\n",
+      "        inequality constraints with only upper bounds:        0\n",
+      "\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.362e-17 (tol = 1.000e-07) r (rel) = 4.406e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "Limited-Memory approximation started; store data at current iterate.\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 0:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   0  2.1942259e-04 0.00e+00 6.38e-03   0.0 0.00e+00    -  0.00e+00 0.00e+00   0 y\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 0 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.0000000000000000e+00\n",
+      "Current fraction-to-the-boundary parameter tau = 0.0000000000000000e+00\n",
+      "\n",
+      "||curr_x||_inf   = 8.6258387565612793e-01\n",
+      "||curr_s||_inf   = 4.5761677855054583e-02\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 9.9994936348413743e-01\n",
+      "||curr_z_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_z_U||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 0:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   2.1942258539174670e-04    2.1942258539174670e-04\n",
+      "Dual infeasibility......:   6.3814363685714692e-03    6.3814363685714692e-03\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.9000001999999998e-01    9.9000001999999998e-01\n",
+      "Overall NLP error.......:   9.9000001999999998e-01    9.9000001999999998e-01\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "Setting mu_max to 4.999777e+02.\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 1 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 8.050304e-03\n",
+      "Barrier parameter mu computed by oracle is 4.024973e-03\n",
+      "Barrier parameter mu after safeguards is 4.024973e-03\n",
+      "Barrier Parameter: 4.024973e-03\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 2.242425e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.121212e-16\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 0:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 0 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 0.000000E+00\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "trial_max is initialized to 1.000000e+04\n",
+      "trial_min is initialized to 1.000000e-04\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.611e-17 (tol = 1.000e-07) r (rel) = 4.657e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  1.4305435094170917e+02  (reference  1.5242228657191637e+02):\n",
+      "  New values of constraint violation =  3.1918911957973251e-16  (reference  0.0000000000000000e+00):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.969198e-03\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.791167e-03 snrm = 3.402711e-01 ynrm = 2.386620e-02\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.546984e-02\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 1:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   1  2.1996526e-04 0.00e+00 3.89e-03  -2.4 3.97e-03    -  9.96e-01 1.00e+00f  1  sigma=8.05e-03 qf=13y \n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 1 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 4.0249730728791578e-03\n",
+      "Current fraction-to-the-boundary parameter tau = 9.8999999999999999e-01\n",
+      "\n",
+      "||curr_x||_inf   = 8.5997292196921093e-01\n",
+      "||curr_s||_inf   = 4.3001541813346658e-02\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.5475754103611994e-01\n",
+      "||curr_z_L||_inf = 1.6325458704352203e-02\n",
+      "||curr_z_U||_inf = 1.4691581823443434e-02\n",
+      "||curr_v_L||_inf = 1.5205840589663955e-01\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 1:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   2.1996525743867739e-04    2.1996525743867739e-04\n",
+      "Dual infeasibility......:   3.8882951455892177e-03    3.8882951455892177e-03\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.2311876287447893e-02    1.2311876287447893e-02\n",
+      "Overall NLP error.......:   1.2311876287447893e-02    1.2311876287447893e-02\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "Switching to fixed mu mode with mu =   4.9890526165039467e-03 and tau =   9.9501094738349605e-01.\n",
+      "Barrier Parameter: 4.989053e-03\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 2.297806e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.160871e-16\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 1:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 1 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.128259E-28\n",
+      "Starting checks for alpha (primal) = 2.08e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.194e-17 (tol = 1.000e-07) r (rel) = 4.236e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 2.083431e-01:\n",
+      "  New values of barrier function     =  1.1845032785048490e+02  (reference  1.7731931590127948e+02):\n",
+      "  New values of constraint violation =  2.0816681711721685e-17  (reference  3.1918911957973251e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 1.494710e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 2.774185e-06 snrm = 5.498271e+00 ynrm = 5.797805e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 9.176627e-08\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 2:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   2  2.3760911e-04 0.00e+00 2.51e-01  -2.3 7.17e-01    -  6.58e-01 2.08e-01f  1 FNhj y \n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 2 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 4.9890526165039467e-03\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9501094738349605e-01\n",
+      "\n",
+      "||curr_x||_inf   = 8.0880013000330819e-01\n",
+      "||curr_s||_inf   = 2.1452700458810675e-04\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 6.0887417125727905e-01\n",
+      "||curr_z_L||_inf = 2.5838055223470702e-01\n",
+      "||curr_z_U||_inf = 1.1512249167357101e-02\n",
+      "||curr_v_L||_inf = 6.0612236954512522e-01\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 2:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   2.3760910728674287e-04    2.3760910728674287e-04\n",
+      "Dual infeasibility......:   2.5107298073720691e-01    2.5107298073720691e-01\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.2863973655349236e-03    9.2863973655349236e-03\n",
+      "Overall NLP error.......:   2.5107298073720691e-01    2.5107298073720691e-01\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Barrier Parameter: 4.989053e-03\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 1.506347e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 3.309846e-19\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 2:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 2 <--\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 9.802541E-29\n",
+      "Starting checks for alpha (primal) = 3.64e-03\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.269e-17 (tol = 1.000e-07) r (rel) = 4.311e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 3.637951e-03:\n",
+      "  New values of barrier function     =  1.1808811368906227e+02  (reference  1.1845032785048490e+02):\n",
+      "  New values of constraint violation =  3.6537613212761499e-17  (reference  2.0816681711721685e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.043233e-03\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 3.282502e-08 snrm = 1.108085e-01 ynrm = 4.660362e-07\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 2.673367e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 3:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   3  2.3993882e-04 0.00e+00 8.67e-02  -2.3 5.62e-01    -  6.55e-01 3.64e-03f  1 Fy \n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 3 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 4.9890526165039467e-03\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9501094738349605e-01\n",
+      "\n",
+      "||curr_x||_inf   = 8.0686176281733890e-01\n",
+      "||curr_s||_inf   = 1.0603364040772225e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.2394395684405953e+02\n",
+      "||curr_z_L||_inf = 1.0500214297429558e-01\n",
+      "||curr_z_U||_inf = 9.5398931984432729e-03\n",
+      "||curr_v_L||_inf = 1.2394300679602811e+02\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 3:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   2.3993881967835905e-04    2.3993881967835905e-04\n",
+      "Dual infeasibility......:   8.6732203803931610e-02    8.6732203803931610e-02\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.8962423191280829e-03    9.8962423191280829e-03\n",
+      "Overall NLP error.......:   8.6732203803931610e-02    8.6732203803931610e-02\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Barrier Parameter: 4.989053e-03\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 1.115028e-13\n",
+      "Factorization successful.\n",
+      "residual_ratio = 5.973366e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 3:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 3 <--\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.319218E-28\n",
+      "Starting checks for alpha (primal) = 3.88e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 3.967e-17 (tol = 1.000e-07) r (rel) = 4.007e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 3.880762e-01:\n",
+      "  New values of barrier function     =  9.8706772844173358e+01  (reference  1.1808811368906227e+02):\n",
+      "  New values of constraint violation =  4.3571374806761209e-18  (reference  3.6537613212761499e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 8.028363e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.918919e-03 snrm = 1.236189e+01 ynrm = 2.175904e-04\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.255702e-05\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 4:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   4  1.3355989e-03 0.00e+00 9.20e-06  -2.3 2.07e+00    -  1.00e+00 3.88e-01f  1 Fy \n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 4 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 4.9890526165039467e-03\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9501094738349605e-01\n",
+      "\n",
+      "||curr_x||_inf   = 5.5392494124768277e-01\n",
+      "||curr_s||_inf   = 1.6289179056517917e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.1706085644763425e+02\n",
+      "||curr_z_L||_inf = 3.6912553191371558e-02\n",
+      "||curr_z_U||_inf = 2.5216305042701351e-02\n",
+      "||curr_v_L||_inf = 1.1706085624371674e+02\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 4:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.3355988667522343e-03    1.3355988667522343e-03\n",
+      "Dual infeasibility......:   9.2036685386646944e-06    9.2036685386646944e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   2.5114797920333456e-02    2.5114797920333456e-02\n",
+      "Overall NLP error.......:   2.5114797920333456e-02    2.5114797920333456e-02\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Reducing mu to   3.5239288103462837e-04 in fixed mu mode. Tau becomes   9.9501094738349605e-01\n",
+      "Barrier Parameter: 3.523929e-04\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 1.380742e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 5.056430e-20\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 4:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 4 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 3.354207E-26\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 3.652e-17 (tol = 1.000e-07) r (rel) = 3.689e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  6.9115959742747517e+00  (reference  6.9732190561774079e+00):\n",
+      "  New values of constraint violation =  1.0638733817514012e-16  (reference  4.3571374806761209e-18):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 1.948300e-02\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 6.321949e-06 snrm = 8.472977e-01 ynrm = 1.652278e-05\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 8.806006e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 5:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   5  1.4661540e-03 0.00e+00 7.92e-07  -3.5 1.95e-02    -  1.00e+00 1.00e+00f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 5 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 3.5239288103462837e-04\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9501094738349605e-01\n",
+      "\n",
+      "||curr_x||_inf   = 5.3499795545488793e-01\n",
+      "||curr_s||_inf   = 1.7327398583034476e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.4163781748571674e+01\n",
+      "||curr_z_L||_inf = 2.0062032473238608e-03\n",
+      "||curr_z_U||_inf = 6.2443924226025967e-04\n",
+      "||curr_v_L||_inf = 1.4163781715087993e+01\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 5:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.4661539609085222e-03    1.4661539609085222e-03\n",
+      "Dual infeasibility......:   7.9207857976975465e-07    7.9207857976975465e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   7.0389347768359540e-04    7.0389347768359540e-04\n",
+      "Overall NLP error.......:   7.0389347768359540e-04    7.0389347768359540e-04\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Reducing mu to   6.6151651268281218e-06 in fixed mu mode. Tau becomes   9.9964760711896539e-01\n",
+      "Barrier Parameter: 6.615165e-06\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 2.556258e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 6.630917e-20\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 5:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 5 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 3.042071E-22\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 3.725e-17 (tol = 1.000e-07) r (rel) = 3.762e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  1.3101096689243513e-01  (reference  1.3118401226436072e-01):\n",
+      "  New values of constraint violation =  1.3203888394978935e-16  (reference  1.0638733817514012e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 4.347495e-03\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.301261e-07 snrm = 1.528487e-01 ynrm = 2.294377e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 5.569817e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 6:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   6  1.4873534e-03 0.00e+00 1.53e-07  -5.2 4.35e-03    -  1.00e+00 1.00e+00f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 6 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 6.6151651268281218e-06\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9964760711896539e-01\n",
+      "\n",
+      "||curr_x||_inf   = 5.3065046069056843e-01\n",
+      "||curr_s||_inf   = 1.7468387203296654e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 2.6637405079446203e-01\n",
+      "||curr_z_L||_inf = 3.4926046848179427e-05\n",
+      "||curr_z_U||_inf = 1.3533433408582875e-05\n",
+      "||curr_v_L||_inf = 2.6637405046696827e-01\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 6:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.4873534193741112e-03    1.4873534193741112e-03\n",
+      "Dual infeasibility......:   1.5262773278245795e-07    1.5262773278245795e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.3808185585908299e-05    1.3808185585908299e-05\n",
+      "Overall NLP error.......:   1.3808185585908299e-05    1.3808185585908299e-05\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Reducing mu to   1.7014180435354828e-08 in fixed mu mode. Tau becomes   9.9999338483487321e-01\n",
+      "Barrier Parameter: 1.701418e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 2.450310e-14\n",
+      "Factorization successful.\n",
+      "residual_ratio = 3.315759e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 6:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 6 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.562717E-22\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 3.027e-17 (tol = 1.000e-07) r (rel) = 3.057e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  1.4654763307904255e-03  (reference  1.8204876780084798e-03):\n",
+      "  New values of constraint violation =  7.7818610930147081e-17  (reference  1.3203888394978935e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.154055e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.338051e-04 snrm = 1.894901e+00 ynrm = 9.053622e-05\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 3.726488e-05\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 7:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   7  1.1281828e-03 0.00e+00 8.67e-06  -7.8 2.15e-01    -  6.66e-01 1.00e+00f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 7 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.7014180435354828e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999338483487321e-01\n",
+      "\n",
+      "||curr_x||_inf   = 7.0345580404420227e-01\n",
+      "||curr_s||_inf   = 1.6338962714518393e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.0097300016250953e-01\n",
+      "||curr_z_L||_inf = 1.3241411509933341e-05\n",
+      "||curr_z_U||_inf = 8.3312899001603065e-06\n",
+      "||curr_v_L||_inf = 1.0097310398607715e-01\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 7:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.1281827552494664e-03    1.1281827552494664e-03\n",
+      "Dual infeasibility......:   8.6695417539875768e-06    8.6695417539875768e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   4.6957154802993652e-06    4.6957154802993652e-06\n",
+      "Overall NLP error.......:   8.6695417539875768e-06    8.6695417539875768e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Barrier Parameter: 1.701418e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 8.819432e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 4.520145e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 7:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 7 <--\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 6.744516E-23\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 2.888e-17 (tol = 1.000e-07) r (rel) = 2.917e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  1.0243513083792409e-03  (reference  1.4654763307904255e-03):\n",
+      "  New values of constraint violation =  7.9968380550278495e-17  (reference  7.7818610930147081e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.287016e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 2.616642e-04 snrm = 3.418644e+00 ynrm = 9.575779e-05\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 2.238910e-05\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 8:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   8  6.7640116e-04 0.00e+00 3.43e-06  -7.8 2.29e-01    -  6.75e-01 1.00e+00f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 8 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.7014180435354828e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999338483487321e-01\n",
+      "\n",
+      "||curr_x||_inf   = 7.6318381280224079e-01\n",
+      "||curr_s||_inf   = 1.4313794211271403e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 4.1955954930602071e-02\n",
+      "||curr_z_L||_inf = 5.4613851005161841e-06\n",
+      "||curr_z_U||_inf = 3.8786726824001930e-06\n",
+      "||curr_v_L||_inf = 4.1956134144022668e-02\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 8:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   6.7640116270946242e-04    6.7640116270946242e-04\n",
+      "Dual infeasibility......:   3.4269238816625711e-06    3.4269238816625711e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.5510633745735747e-06    1.5510633745735747e-06\n",
+      "Overall NLP error.......:   3.4269238816625711e-06    3.4269238816625711e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Barrier Parameter: 1.701418e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 6.428517e-17\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.552143e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 8:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 8 <--\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.119770E-22\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.086e-17 (tol = 1.000e-07) r (rel) = 4.127e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  7.6709862687967948e-04  (reference  1.0243513083792409e-03):\n",
+      "  New values of constraint violation =  5.2873490633507936e-17  (reference  7.9968380550278495e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.264424e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.812850e-04 snrm = 3.825208e+00 ynrm = 5.801809e-05\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.238944e-05\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 9:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   9  4.0408975e-04 0.00e+00 2.00e-06  -7.8 2.26e-01    -  6.77e-01 1.00e+00f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 9 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.7014180435354828e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999338483487321e-01\n",
+      "\n",
+      "||curr_x||_inf   = 8.3610688521683940e-01\n",
+      "||curr_s||_inf   = 1.2198011328926200e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.8543169942774943e-02\n",
+      "||curr_z_L||_inf = 2.4280079495006772e-06\n",
+      "||curr_z_U||_inf = 2.1094055843836140e-06\n",
+      "||curr_v_L||_inf = 1.8543289387852560e-02\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 9:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   4.0408974679138210e-04    4.0408974679138210e-04\n",
+      "Dual infeasibility......:   1.9989240045191167e-06    1.9989240045191167e-06\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   5.0899224309982139e-07    5.0899224309982139e-07\n",
+      "Overall NLP error.......:   1.9989240045191167e-06    1.9989240045191167e-06\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Barrier Parameter: 1.701418e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 5.058862e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 3.842626e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 9:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 9 <--\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.658964E-22\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 3.928e-17 (tol = 1.000e-07) r (rel) = 3.967e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  6.5082081308500414e-04  (reference  7.6709862687967948e-04):\n",
+      "  New values of constraint violation =  1.0839988845781634e-16  (reference  5.2873490633507936e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.203736e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 7.598389e-05 snrm = 3.435318e+00 ynrm = 2.799207e-05\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 6.438544e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 10:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  10  2.7242392e-04 0.00e+00 6.21e-07  -7.8 2.20e-01    -  7.38e-01 1.00e+00f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 10 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.7014180435354828e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999338483487321e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.4014453591154956e-01\n",
+      "||curr_s||_inf   = 1.0673024993193405e-05\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 7.6009501801899424e-03\n",
+      "||curr_z_L||_inf = 9.3432526052621185e-07\n",
+      "||curr_z_U||_inf = 1.3140704048849144e-06\n",
+      "||curr_v_L||_inf = 7.6009746070818448e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 10:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   2.7242392407408265e-04    2.7242392407408265e-04\n",
+      "Dual infeasibility......:   6.2094039813404981e-07    6.2094039813404981e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.5917165117875071e-07    1.5917165117875071e-07\n",
+      "Overall NLP error.......:   6.2094039813404981e-07    6.2094039813404981e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Barrier Parameter: 1.701418e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 4.709658e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.051788e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 10:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 10 <--\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 6.601801E-22\n",
+      "Starting checks for alpha (primal) = 1.00e+00\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.378e-17 (tol = 1.000e-07) r (rel) = 4.421e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 1.000000e+00:\n",
+      "  New values of barrier function     =  5.9273716445835674e-04  (reference  6.5082081308500414e-04):\n",
+      "  New values of constraint violation =  1.1466623820160365e-16  (reference  1.0839988845781634e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.164711e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 3.957596e-05 snrm = 3.529055e+00 ynrm = 1.467257e-05\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 3.177712e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 11:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  11  1.9527707e-04 0.00e+00 4.45e-07  -7.8 2.16e-01    -  7.91e-01 1.00e+00f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 11 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.7014180435354828e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999338483487321e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.8028939099711931e-01\n",
+      "||curr_s||_inf   = 9.2599494043480248e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 3.6455192022053213e-03\n",
+      "||curr_z_L||_inf = 4.8196587118703776e-07\n",
+      "||curr_z_U||_inf = 1.1127746763234580e-06\n",
+      "||curr_v_L||_inf = 3.6455212099056264e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 11:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.9527707084811123e-04    1.9527707084811123e-04\n",
+      "Dual infeasibility......:   4.4465084158661657e-07    4.4465084158661657e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   5.5003288128345442e-08    5.5003288128345442e-08\n",
+      "Overall NLP error.......:   4.4465084158661657e-07    4.4465084158661657e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "Switching back to free mu mode.\n",
+      "The current filter has 2 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 5.016691e-01\n",
+      "Barrier parameter mu computed by oracle is 1.575817e-08\n",
+      "Barrier parameter mu after safeguards is 1.575817e-08\n",
+      "Barrier Parameter: 1.575817e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 1.135916e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 2.293659e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 11:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 11 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 1.558537E-21\n",
+      "Starting checks for alpha (primal) = 8.10e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.590e-17 (tol = 1.000e-07) r (rel) = 4.636e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 8.099143e-01:\n",
+      "  New values of barrier function     =  5.4196131083041141e-04  (reference  5.6339617773549589e-04):\n",
+      "  New values of constraint violation =  7.9231461886167254e-18  (reference  1.1466623820160365e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.226766e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.152956e-05 snrm = 2.751114e+00 ynrm = 6.415046e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.523335e-06\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 12:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  12  1.5958699e-04 0.00e+00 4.45e-07  -7.8 2.75e-01    -  9.57e-01 8.10e-01f  1  sigma=5.02e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 12 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.5758172976292438e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999955534915841e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999997055548617e-01\n",
+      "||curr_s||_inf   = 8.4358719887798899e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 2.1661673739142989e-03\n",
+      "||curr_z_L||_inf = 2.6202759839735379e-07\n",
+      "||curr_z_U||_inf = 9.9235312665097110e-07\n",
+      "||curr_v_L||_inf = 2.1661640754923489e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 12:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.5958699244895272e-04    1.5958699244895272e-04\n",
+      "Dual infeasibility......:   4.4549012189946007e-07    4.4549012189946007e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   2.4593570994306414e-08    2.4593570994306414e-08\n",
+      "Overall NLP error.......:   4.4549012189946007e-07    4.4549012189946007e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 2 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 6.721865e-01\n",
+      "Barrier parameter mu computed by oracle is 1.152566e-08\n",
+      "Barrier parameter mu after safeguards is 1.152566e-08\n",
+      "Barrier Parameter: 1.152566e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 6.393566e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.083293e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 12:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 12 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 8.227417E-25\n",
+      "Starting checks for alpha (primal) = 5.24e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.631e-17 (tol = 1.000e-07) r (rel) = 4.677e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 5.235646e-01:\n",
+      "  New values of barrier function     =  4.2726855941740125e-04  (reference  4.3925886798422908e-04):\n",
+      "  New values of constraint violation =  5.6405618023558368e-17  (reference  7.9231461886167254e-18):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 2.822359e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 5.222832e-06 snrm = 2.470537e+00 ynrm = 3.651876e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 8.557038e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 13:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  13  1.3948467e-04 0.00e+00 4.86e-07  -7.9 5.39e-01    -  9.74e-01 5.24e-01f  1  sigma=6.72e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 13 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.1525663674128999e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999955450987810e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999996479060049e-01\n",
+      "||curr_s||_inf   = 7.8083502085814986e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.6847635334661124e-03\n",
+      "||curr_z_L||_inf = 1.7914171487451332e-07\n",
+      "||curr_z_U||_inf = 7.6500365446862860e-07\n",
+      "||curr_v_L||_inf = 1.6847664142600424e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 13:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.3948467146953391e-04    1.3948467146953391e-04\n",
+      "Dual infeasibility......:   4.8578582202864654e-07    4.8578582202864654e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.7595471822780307e-08    1.7595471822780307e-08\n",
+      "Overall NLP error.......:   4.8578582202864654e-07    4.8578582202864654e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 3 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 7.855842e-01\n",
+      "Barrier parameter mu computed by oracle is 9.615269e-09\n",
+      "Barrier parameter mu after safeguards is 9.615269e-09\n",
+      "Barrier Parameter: 9.615269e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 3.610841e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 3.541318e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 13:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 13 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 6.611108E-24\n",
+      "Starting checks for alpha (primal) = 4.55e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.508e-17 (tol = 1.000e-07) r (rel) = 4.553e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 4.548408e-01:\n",
+      "  New values of barrier function     =  3.7245782496073300e-04  (reference  3.7956798930867018e-04):\n",
+      "  New values of constraint violation =  1.8598725939336300e-16  (reference  5.6405618023558368e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 4.721130e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 4.242770e-06 snrm = 2.565256e+00 ynrm = 2.875115e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 6.447449e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 14:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  14  1.2621178e-04 0.00e+00 3.96e-07  -8.0 1.04e+00    -  9.58e-01 4.55e-01f  1  sigma=7.86e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 14 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 9.6152692734631776e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999951421417799e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999978065406925e-01\n",
+      "||curr_s||_inf   = 7.4034442845067021e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.4328379383057319e-03\n",
+      "||curr_z_L||_inf = 1.5608002472099507e-07\n",
+      "||curr_z_U||_inf = 6.1740132502512337e-07\n",
+      "||curr_v_L||_inf = 1.4328426458293669e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 14:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.2621178334637219e-04    1.2621178334637219e-04\n",
+      "Dual infeasibility......:   3.9574454545733103e-07    3.9574454545733103e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.7440758031090139e-08    1.7440758031090139e-08\n",
+      "Overall NLP error.......:   3.9574454545733103e-07    3.9574454545733103e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 4 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 7.435140e-01\n",
+      "Barrier parameter mu computed by oracle is 7.551049e-09\n",
+      "Barrier parameter mu after safeguards is 7.551049e-09\n",
+      "Barrier Parameter: 7.551049e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 3.488421e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 5.261785e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 14:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 14 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 3.214483E-23\n",
+      "Starting checks for alpha (primal) = 5.26e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.659e-17 (tol = 1.000e-07) r (rel) = 4.706e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 5.256877e-01:\n",
+      "  New values of barrier function     =  3.1554774277217667e-04  (reference  3.1959335769371814e-04):\n",
+      "  New values of constraint violation =  1.6286495399921511e-16  (reference  1.8598725939336300e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 5.254068e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 2.875557e-06 snrm = 2.448011e+00 ynrm = 2.082392e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 4.798385e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 15:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  15  1.1765070e-04 0.00e+00 2.67e-07  -8.1 9.99e-01    -  9.54e-01 5.26e-01f  1  sigma=7.44e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 15 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 7.5510489333588987e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999960425545453e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999980207303851e-01\n",
+      "||curr_s||_inf   = 7.0830367970601319e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.1499145754809742e-03\n",
+      "||curr_z_L||_inf = 1.3396638055900011e-07\n",
+      "||curr_z_U||_inf = 5.2970059904212701e-07\n",
+      "||curr_v_L||_inf = 1.1499172528534000e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 15:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.1765070036925208e-04    1.1765070036925208e-04\n",
+      "Dual infeasibility......:   2.6653865673269954e-07    2.6653865673269954e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.1173916830442450e-08    1.1173916830442450e-08\n",
+      "Overall NLP error.......:   2.6653865673269954e-07    2.6653865673269954e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 4 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 8.116906e-01\n",
+      "Barrier parameter mu computed by oracle is 6.445589e-09\n",
+      "Barrier parameter mu after safeguards is 6.445589e-09\n",
+      "Barrier Parameter: 6.445589e-09\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 7.993938e-17\n",
+      "Factorization successful.\n",
+      "residual_ratio = 5.421888e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 15:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 15 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.268156E-23\n",
+      "Starting checks for alpha (primal) = 6.04e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.458e-17 (tol = 1.000e-07) r (rel) = 4.503e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 6.039029e-01:\n",
+      "  New values of barrier function     =  2.8417270300612387e-04  (reference  2.8657596712624201e-04):\n",
+      "  New values of constraint violation =  6.0164750243472953e-17  (reference  1.6286495399921511e-16):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 4.934272e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.791212e-06 snrm = 2.365173e+00 ynrm = 1.571980e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 3.201999e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 16:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  16  1.1162605e-04 0.00e+00 2.14e-07  -8.2 8.17e-01    -  9.83e-01 6.04e-01f  1  sigma=8.12e-01 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 16 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 6.4455887762367371e-09\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999973346134330e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.9999989002993517e-01\n",
+      "||curr_s||_inf   = 6.8155430864811444e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 9.8338734956333017e-04\n",
+      "||curr_z_L||_inf = 1.1945892901526540e-07\n",
+      "||curr_z_U||_inf = 4.7281830816883926e-07\n",
+      "||curr_v_L||_inf = 9.8338980961508470e-04\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 16:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.1162604642663981e-04    1.1162604642663981e-04\n",
+      "Dual infeasibility......:   2.1386151081315249e-07    2.1386151081315249e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.1523551347103779e-08    1.1523551347103779e-08\n",
+      "Overall NLP error.......:   2.1386151081315249e-07    2.1386151081315249e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "Staying in free mu mode.\n",
+      "The current filter has 4 entries.\n",
+      "Solving the Primal Dual System for the affine step\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "Solving the Primal Dual System for the centering step\n",
+      "Factorization successful.\n",
+      "Sigma = 3.912269e+00\n",
+      "Barrier parameter mu computed by oracle is 2.587262e-08\n",
+      "Barrier parameter mu after safeguards is 2.587262e-08\n",
+      "Barrier Parameter: 2.587262e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "residual_ratio = 4.525944e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 2.157766e-17\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 16:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 16 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.607848E-25\n",
+      "Starting checks for alpha (primal) = 2.48e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.405e-17 (tol = 1.000e-07) r (rel) = 4.448e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 2.484775e-01:\n",
+      "  New values of barrier function     =  7.8601601938563411e-04  (reference  8.0422906148101391e-04):\n",
+      "  New values of constraint violation =  1.1411227865409934e-17  (reference  6.0164750243472953e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 17:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.762147e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 1.287324e-06 snrm = 2.435939e+00 ynrm = 2.102636e-06\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 2.169478e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 17:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  17  1.2176570e-04 0.00e+00 2.90e-07  -7.6 1.51e+00    -  4.41e-01 2.48e-01f  1  sigma=3.91e+00 qf=12y A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 17 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 2.5872620824534707e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999978613848917e-01\n",
+      "\n",
+      "||curr_x||_inf   = 9.7740280907595700e-01\n",
+      "||curr_s||_inf   = 7.8561094680576796e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.9547373223582206e-03\n",
+      "||curr_z_L||_inf = 2.1618621787071527e-07\n",
+      "||curr_z_U||_inf = 6.4135637932442364e-07\n",
+      "||curr_v_L||_inf = 1.9547572099455936e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 17:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.2176570302348460e-04    1.2176570302348460e-04\n",
+      "Dual infeasibility......:   2.9043384504889128e-07    2.9043384504889128e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.7831998442378964e-08    1.7831998442378964e-08\n",
+      "Overall NLP error.......:   2.9043384504889128e-07    2.9043384504889128e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 17:\n",
+      "**************************************************\n",
+      "\n",
+      "Switching to fixed mu mode with mu =   1.2027873062894477e-08 and tau =   9.9999998797212697e-01.\n",
+      "Barrier Parameter: 1.202787e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 17:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 8.182896e-16\n",
+      "Factorization successful.\n",
+      "residual_ratio = 8.066136e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 17:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 17 <--\n",
+      "Mu has changed in line search - resetting watchdog counters.\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.076403E-25\n",
+      "Starting checks for alpha (primal) = 4.47e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.714e-17 (tol = 1.000e-07) r (rel) = 4.761e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 4.473861e-01:\n",
+      "  New values of barrier function     =  4.2888491163681197e-04  (reference  4.3056775834580680e-04):\n",
+      "  New values of constraint violation =  7.8828274203274207e-17  (reference  1.1411227865409934e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update HessianMatrix for Iteration 18:\n",
+      "**************************************************\n",
+      "\n",
+      "In limited-memory update, s_new_max is 3.499704e-01\n",
+      "Limited-Memory test for skipping:\n",
+      "     s^Ty = 3.028295e-07 snrm = 1.297182e+00 ynrm = 7.843733e-07\n",
+      "     Perform the update.\n",
+      "sigma (for B0) is 1.799683e-07\n",
+      "Number of successive iterations with skipping: 0\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 18:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  18  1.2247828e-04 0.00e+00 1.96e-07  -7.9 7.82e-01    -  5.57e-01 4.47e-01f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 18 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.2027873062894477e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999998797212697e-01\n",
+      "\n",
+      "||curr_x||_inf   = 1.0000000083740865e+00\n",
+      "||curr_s||_inf   = 8.1215562449621383e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.6355194586317435e-03\n",
+      "||curr_z_L||_inf = 1.7926883243309775e-07\n",
+      "||curr_z_U||_inf = 5.4977433490897253e-07\n",
+      "||curr_v_L||_inf = 1.6355299342325469e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 18:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.2247828477481950e-04    1.2247828477481950e-04\n",
+      "Dual infeasibility......:   1.9594179028080537e-07    1.9594179028080537e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.5196264624183976e-08    1.5196264624183976e-08\n",
+      "Overall NLP error.......:   1.9594179028080537e-07    1.9594179028080537e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Update Barrier Parameter for Iteration 18:\n",
+      "**************************************************\n",
+      "\n",
+      "Remaining in fixed mu mode.\n",
+      "Barrier Parameter: 1.202787e-08\n",
+      "\n",
+      "**************************************************\n",
+      "*** Solving the Primal Dual System for Iteration 18:\n",
+      "**************************************************\n",
+      "\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Factorization successful.\n",
+      "Number of trial factorizations performed: 1\n",
+      "Perturbation parameters: delta_x=0.000000e+00 delta_s=0.000000e+00\n",
+      "                         delta_c=0.000000e+00 delta_d=0.000000e+00\n",
+      "residual_ratio = 2.419797e-15\n",
+      "Factorization successful.\n",
+      "residual_ratio = 1.197977e-18\n",
+      "\n",
+      "**************************************************\n",
+      "*** Finding Acceptable Trial Point for Iteration 18:\n",
+      "**************************************************\n",
+      "\n",
+      "--> Starting line search in iteration 18 <--\n",
+      "Storing current iterate as backup acceptable point.\n",
+      "The current filter has 0 entries.\n",
+      "minimal step size ALPHA_MIN = 2.195335E-26\n",
+      "Starting checks for alpha (primal) = 2.83e-01\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.637e-17 (tol = 1.000e-07) r (rel) = 4.683e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Checking acceptability for trial step size alpha_primal_test= 2.828678e-01:\n",
+      "  New values of barrier function     =  4.2773294117671708e-04  (reference  4.2888491163681197e-04):\n",
+      "  New values of constraint violation =  7.0047930672036129e-17  (reference  7.8828274203274207e-17):\n",
+      "Checking Armijo Condition...\n",
+      "Succeeded...\n",
+      "Checking filter acceptability...\n",
+      "Succeeded...\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 19:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "  19  1.2297888e-04 0.00e+00 2.14e-07  -7.9 9.43e-01    -  5.86e-01 2.83e-01f  1 Fy A\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 19 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.2027873062894477e-08\n",
+      "Current fraction-to-the-boundary parameter tau = 9.9999998797212697e-01\n",
+      "\n",
+      "||curr_x||_inf   = 1.0000000078808737e+00\n",
+      "||curr_s||_inf   = 8.3286158090855116e-06\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 1.4577633268749483e-03\n",
+      "||curr_z_L||_inf = 1.5984297319862014e-07\n",
+      "||curr_z_U||_inf = 5.2689990964864298e-07\n",
+      "||curr_v_L||_inf = 1.4577685220642841e-03\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 19:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.2297888298278239e-04    1.2297888298278239e-04\n",
+      "Dual infeasibility......:   2.1443637202631501e-07    2.1443637202631501e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.5730157321749088e-08    1.5730157321749088e-08\n",
+      "Overall NLP error.......:   2.1443637202631501e-07    2.1443637202631501e-07\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "Number of Iterations....: 19\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.2297888298278239e-04    1.2297888298278239e-04\n",
+      "Dual infeasibility......:   2.1443637202631501e-07    2.1443637202631501e-07\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   1.5730157321749088e-08    1.5730157321749088e-08\n",
+      "Overall NLP error.......:   2.1443637202631501e-07    2.1443637202631501e-07\n",
+      "\n",
+      "\n",
+      "Number of objective function evaluations             = 20\n",
+      "Number of objective gradient evaluations             = 20\n",
+      "Number of equality constraint evaluations            = 0\n",
+      "Number of inequality constraint evaluations          = 20\n",
+      "Number of equality constraint Jacobian evaluations   = 0\n",
+      "Number of inequality constraint Jacobian evaluations = 20\n",
+      "Number of Lagrangian Hessian evaluations             = 0\n",
+      "Total CPU secs in IPOPT (w/o function evaluations)   =      2.151\n",
+      "Total CPU secs in NLP function evaluations           =      2.255\n",
+      "\n",
+      "EXIT: Solved To Acceptable Level.\n",
+      "v=0.2\n",
+      "w=0.1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "rm: cannot remove '/home/fenics/shared/templates/RES_OPT/TEMP*': No such file or directory\n"
+     ]
+    }
+   ],
+   "source": [
+    "opt_designs = []\n",
+    "opt_histories = []\n",
+    "for i in range(n_samples):\n",
+    "    opt_design, opt_history = problem.optimize(\n",
+    "        starting_point=gen_designs[i],\n",
+    "        config=conditions[i],\n",
+    "    )\n",
+    "    opt_histories.append(opt_history)\n",
+    "    opt_designs.append(opt_design)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Eyeball testing the model\n",
+    "\n",
+    "Render the optimal designs next to the generated designs to get a visual idea"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(n_samples):\n",
+    "    fig_opt, ax_opt = problem.render(opt_designs[i])\n",
+    "    fig_gen, ax_gen = problem.render(gen_designs[i])\n",
+    "    ax_opt.set_title(\"Optimal\")\n",
+    "    ax_gen.set_title(\"Generated\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Qualitative evaluation\n",
+    "\n",
+    "Now we can also use metrics to get a quantitative idea of how good the generated designs are. Here we compute optimality gaps between the generated designs and optimized designs in terms of objective values (thermal compliance for heatconduction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 6.329e-17 (tol = 1.000e-07) r (rel) = 6.389e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "List of options:\n",
+      "\n",
+      "                                    Name   Value                # times used\n",
+      "                          acceptable_tol = 0.001                     1\n",
+      "                   hessian_approximation = limited-memory            7\n",
+      "                                max_iter = 0                         1\n",
+      "                             print_level = 6                         2\n",
+      "\n",
+      "******************************************************************************\n",
+      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
+      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
+      "         For more information visit http://projects.coin-or.org/Ipopt\n",
+      "******************************************************************************\n",
+      "\n",
+      "This is Ipopt version 3.12.9, running with linear solver mumps.\n",
+      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
+      "\n",
+      "Number of nonzeros in equality constraint Jacobian...:        0\n",
+      "Number of nonzeros in inequality constraint Jacobian.:    10201\n",
+      "Number of nonzeros in Lagrangian Hessian.............:        0\n",
+      "\n",
+      "Hessian approximation will be done in the space of all 10201 x variables.\n",
+      "\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Scaling parameter for objective function = 1.000000e+00\n",
+      "objective scaling factor = 1\n",
+      "No x scaling provided\n",
+      "No c scaling provided\n",
+      "No d scaling provided\n",
+      "Moved initial values of x sufficiently inside the bounds.\n",
+      "Moved initial values of s sufficiently inside the bounds.\n",
+      "MUMPS used permuting_scaling 5 and pivot_order 5.\n",
+      "           scaling will be 77.\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Least square estimates max(y_c) = 0.000000e+00, max(y_d) = 9.999010e-01\n",
+      "Total number of variables............................:    10201\n",
+      "                     variables with only lower bounds:        0\n",
+      "                variables with lower and upper bounds:    10201\n",
+      "                     variables with only upper bounds:        0\n",
+      "Total number of equality constraints.................:        0\n",
+      "Total number of inequality constraints...............:        1\n",
+      "        inequality constraints with only lower bounds:        1\n",
+      "   inequality constraints with lower and upper bounds:        0\n",
+      "        inequality constraints with only upper bounds:        0\n",
+      "\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.906e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 5.466e-17 (tol = 1.000e-07) r (rel) = 5.518e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 0:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   0  6.3524891e-05 0.00e+00 1.00e-04   0.0 0.00e+00    -  0.00e+00 0.00e+00   0 y\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 0 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.0000000000000000e+00\n",
+      "Current fraction-to-the-boundary parameter tau = 0.0000000000000000e+00\n",
+      "\n",
+      "||curr_x||_inf   = 9.9000000989999992e-01\n",
+      "||curr_s||_inf   = 9.9999900000000003e-03\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 9.9990103626823723e-01\n",
+      "||curr_z_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_z_U||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 0:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   6.3524891326338607e-05    6.3524891326338607e-05\n",
+      "Dual infeasibility......:   1.0001097677203585e-04    1.0001097677203585e-04\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.9000001989999997e-01    9.9000001989999997e-01\n",
+      "Overall NLP error.......:   9.9000001989999997e-01    9.9000001989999997e-01\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "Number of Iterations....: 0\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   6.3524891326338607e-05    6.3524891326338607e-05\n",
+      "Dual infeasibility......:   1.0001097677203585e-04    1.0001097677203585e-04\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.9000001989999997e-01    9.9000001989999997e-01\n",
+      "Overall NLP error.......:   9.9000001989999997e-01    9.9000001989999997e-01\n",
+      "\n",
+      "\n",
+      "Number of objective function evaluations             = 1\n",
+      "Number of objective gradient evaluations             = 1\n",
+      "Number of equality constraint evaluations            = 0\n",
+      "Number of inequality constraint evaluations          = 1\n",
+      "Number of equality constraint Jacobian evaluations   = 0\n",
+      "Number of inequality constraint Jacobian evaluations = 1\n",
+      "Number of Lagrangian Hessian evaluations             = 0\n",
+      "Total CPU secs in IPOPT (w/o function evaluations)   =      0.229\n",
+      "Total CPU secs in NLP function evaluations           =      0.136\n",
+      "\n",
+      "EXIT: Maximum Number of Iterations Exceeded.\n",
+      "Optimization complete: v=0.3, w=0.5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "rm: cannot remove '/home/fenics/shared/templates/RES_SIM/TEMP*': No such file or directory\n",
+      "WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.882e-17 (tol = 1.000e-07) r (rel) = 4.927e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "List of options:\n",
+      "\n",
+      "                                    Name   Value                # times used\n",
+      "                          acceptable_tol = 0.001                     1\n",
+      "                   hessian_approximation = limited-memory            7\n",
+      "                                max_iter = 0                         1\n",
+      "                             print_level = 6                         2\n",
+      "\n",
+      "******************************************************************************\n",
+      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
+      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
+      "         For more information visit http://projects.coin-or.org/Ipopt\n",
+      "******************************************************************************\n",
+      "\n",
+      "This is Ipopt version 3.12.9, running with linear solver mumps.\n",
+      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
+      "\n",
+      "Number of nonzeros in equality constraint Jacobian...:        0\n",
+      "Number of nonzeros in inequality constraint Jacobian.:    10201\n",
+      "Number of nonzeros in Lagrangian Hessian.............:        0\n",
+      "\n",
+      "Hessian approximation will be done in the space of all 10201 x variables.\n",
+      "\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Scaling parameter for objective function = 1.000000e+00\n",
+      "objective scaling factor = 1\n",
+      "No x scaling provided\n",
+      "No c scaling provided\n",
+      "No d scaling provided\n",
+      "Moved initial values of x sufficiently inside the bounds.\n",
+      "Moved initial values of s sufficiently inside the bounds.\n",
+      "MUMPS used permuting_scaling 5 and pivot_order 5.\n",
+      "           scaling will be 77.\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Least square estimates max(y_c) = 0.000000e+00, max(y_d) = 9.999011e-01\n",
+      "Total number of variables............................:    10201\n",
+      "                     variables with only lower bounds:        0\n",
+      "                variables with lower and upper bounds:    10201\n",
+      "                     variables with only upper bounds:        0\n",
+      "Total number of equality constraints.................:        0\n",
+      "Total number of inequality constraints...............:        1\n",
+      "        inequality constraints with only lower bounds:        1\n",
+      "   inequality constraints with lower and upper bounds:        0\n",
+      "        inequality constraints with only upper bounds:        0\n",
+      "\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.909e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.597e-17 (tol = 1.000e-07) r (rel) = 4.640e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 0:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   0  8.1159905e-05 0.00e+00 1.00e-04   0.0 0.00e+00    -  0.00e+00 0.00e+00   0 y\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 0 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.0000000000000000e+00\n",
+      "Current fraction-to-the-boundary parameter tau = 0.0000000000000000e+00\n",
+      "\n",
+      "||curr_x||_inf   = 9.9000000989999992e-01\n",
+      "||curr_s||_inf   = 9.9999900000000003e-03\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 9.9990106716702110e-01\n",
+      "||curr_z_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_z_U||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 0:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   8.1159905148195084e-05    8.1159905148195084e-05\n",
+      "Dual infeasibility......:   9.9989610066741896e-05    9.9989610066741896e-05\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.9000001999999998e-01    9.9000001999999998e-01\n",
+      "Overall NLP error.......:   9.9000001999999998e-01    9.9000001999999998e-01\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "Number of Iterations....: 0\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   8.1159905148195084e-05    8.1159905148195084e-05\n",
+      "Dual infeasibility......:   9.9989610066741896e-05    9.9989610066741896e-05\n",
+      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
+      "Complementarity.........:   9.9000001999999998e-01    9.9000001999999998e-01\n",
+      "Overall NLP error.......:   9.9000001999999998e-01    9.9000001999999998e-01\n",
+      "\n",
+      "\n",
+      "Number of objective function evaluations             = 1\n",
+      "Number of objective gradient evaluations             = 1\n",
+      "Number of equality constraint evaluations            = 0\n",
+      "Number of inequality constraint evaluations          = 1\n",
+      "Number of equality constraint Jacobian evaluations   = 0\n",
+      "Number of inequality constraint Jacobian evaluations = 1\n",
+      "Number of Lagrangian Hessian evaluations             = 0\n",
+      "Total CPU secs in IPOPT (w/o function evaluations)   =      0.229\n",
+      "Total CPU secs in NLP function evaluations           =      0.135\n",
+      "\n",
+      "EXIT: Maximum Number of Iterations Exceeded.\n",
+      "Optimization complete: v=0.4, w=0.7\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "rm: cannot remove '/home/fenics/shared/templates/RES_SIM/TEMP*': No such file or directory\n",
+      "WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.645e-17 (tol = 1.000e-07) r (rel) = 4.691e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "List of options:\n",
+      "\n",
+      "                                    Name   Value                # times used\n",
+      "                          acceptable_tol = 0.001                     1\n",
+      "                   hessian_approximation = limited-memory            7\n",
+      "                                max_iter = 0                         1\n",
+      "                             print_level = 6                         2\n",
+      "\n",
+      "******************************************************************************\n",
+      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
+      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
+      "         For more information visit http://projects.coin-or.org/Ipopt\n",
+      "******************************************************************************\n",
+      "\n",
+      "This is Ipopt version 3.12.9, running with linear solver mumps.\n",
+      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
+      "\n",
+      "Number of nonzeros in equality constraint Jacobian...:        0\n",
+      "Number of nonzeros in inequality constraint Jacobian.:    10201\n",
+      "Number of nonzeros in Lagrangian Hessian.............:        0\n",
+      "\n",
+      "Hessian approximation will be done in the space of all 10201 x variables.\n",
+      "\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "Scaling parameter for objective function = 1.000000e+00\n",
+      "objective scaling factor = 1\n",
+      "No x scaling provided\n",
+      "No c scaling provided\n",
+      "No d scaling provided\n",
+      "Moved initial values of x sufficiently inside the bounds.\n",
+      "Moved initial values of s sufficiently inside the bounds.\n",
+      "MUMPS used permuting_scaling 5 and pivot_order 5.\n",
+      "           scaling will be 77.\n",
+      "Number of doubles for MUMPS to hold factorization (INFO(9)) = 20406\n",
+      "Number of integers for MUMPS to hold factorization (INFO(10)) = 244848\n",
+      "Factorization successful.\n",
+      "Least square estimates max(y_c) = 0.000000e+00, max(y_d) = 9.999011e-01\n",
+      "Total number of variables............................:    10201\n",
+      "                     variables with only lower bounds:        0\n",
+      "                variables with lower and upper bounds:    10201\n",
+      "                     variables with only upper bounds:        0\n",
+      "Total number of equality constraints.................:        0\n",
+      "Total number of inequality constraints...............:        1\n",
+      "        inequality constraints with only lower bounds:        1\n",
+      "   inequality constraints with lower and upper bounds:        0\n",
+      "        inequality constraints with only upper bounds:        0\n",
+      "\n",
+      "No Jacobian form specified for nonlinear variational problem.\n",
+      "Differentiating residual form F to obtain Jacobian J = F'.\n",
+      "Solving nonlinear variational problem.\n",
+      "  Newton iteration 0: r (abs) = 9.901e-05 (tol = 1.000e-07) r (rel) = 1.000e+00 (tol = 1.000e-09)\n",
+      "  Newton iteration 1: r (abs) = 4.887e-17 (tol = 1.000e-07) r (rel) = 4.935e-13 (tol = 1.000e-09)\n",
+      "  Newton solver finished in 1 iterations and 1 linear solver iterations.\n",
+      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
+      "\n",
+      "\n",
+      "**************************************************\n",
+      "*** Summary of Iteration: 0:\n",
+      "**************************************************\n",
+      "\n",
+      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
+      "   0  1.3010401e-04 1.42e-03 1.00e-04   0.0 0.00e+00    -  0.00e+00 0.00e+00   0 y\n",
+      "\n",
+      "**************************************************\n",
+      "*** Beginning Iteration 0 from the following point:\n",
+      "**************************************************\n",
+      "\n",
+      "Current barrier parameter mu = 1.0000000000000000e+00\n",
+      "Current fraction-to-the-boundary parameter tau = 0.0000000000000000e+00\n",
+      "\n",
+      "||curr_x||_inf   = 9.9000000989999992e-01\n",
+      "||curr_s||_inf   = 9.9999900000000003e-03\n",
+      "||curr_y_c||_inf = 0.0000000000000000e+00\n",
+      "||curr_y_d||_inf = 9.9990105500056670e-01\n",
+      "||curr_z_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_z_U||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_L||_inf = 1.0000000000000000e+00\n",
+      "||curr_v_U||_inf = 0.0000000000000000e+00\n",
+      "\n",
+      "\n",
+      "***Current NLP Values for Iteration 0:\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.3010401228781608e-04    1.3010401228781608e-04\n",
+      "Dual infeasibility......:   9.9994541991166308e-05    9.9994541991166308e-05\n",
+      "Constraint violation....:   1.4202672724238034e-03    1.4202672724238034e-03\n",
+      "Complementarity.........:   9.9000001989999997e-01    9.9000001989999997e-01\n",
+      "Overall NLP error.......:   9.9000001989999997e-01    9.9000001989999997e-01\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "Number of Iterations....: 0\n",
+      "\n",
+      "                                   (scaled)                 (unscaled)\n",
+      "Objective...............:   1.3010401228781608e-04    1.3010401228781608e-04\n",
+      "Dual infeasibility......:   9.9994541991166308e-05    9.9994541991166308e-05\n",
+      "Constraint violation....:   1.4202672724238034e-03    1.4202672724238034e-03\n",
+      "Complementarity.........:   9.9000001989999997e-01    9.9000001989999997e-01\n",
+      "Overall NLP error.......:   9.9000001989999997e-01    9.9000001989999997e-01\n",
+      "\n",
+      "\n",
+      "Number of objective function evaluations             = 1\n",
+      "Number of objective gradient evaluations             = 1\n",
+      "Number of equality constraint evaluations            = 0\n",
+      "Number of inequality constraint evaluations          = 1\n",
+      "Number of equality constraint Jacobian evaluations   = 0\n",
+      "Number of inequality constraint Jacobian evaluations = 1\n",
+      "Number of Lagrangian Hessian evaluations             = 0\n",
+      "Total CPU secs in IPOPT (w/o function evaluations)   =      0.235\n",
+      "Total CPU secs in NLP function evaluations           =      0.131\n",
+      "\n",
+      "EXIT: Maximum Number of Iterations Exceeded.\n",
+      "Optimization complete: v=0.2, w=0.1\n",
+      "\u001b[96mInitial optimality gaps: [0.00012219959867, 0.00052516607485, 8.931857771e-05]\u001b[0m\n",
+      "\u001b[96mCumulative optimality gaps: [0.0012739121460599997, 0.0041678009641500004, 0.006302119584199999]\u001b[0m\n",
+      "\u001b[96mFinal optimality gaps: [-2.487013330000005e-06, -1.848306149999991e-06, -7.125132289999996e-06]\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "rm: cannot remove '/home/fenics/shared/templates/RES_SIM/TEMP*': No such file or directory\n"
+     ]
+    }
+   ],
+   "source": [
+    "objective_values_opt = [problem.simulate(opt_designs[i], config=conditions[i]) for i in range(n_samples)]\n",
+    "optimality_gaps = [metrics.optimality_gap(opt_histories[i], baseline=objective_values_opt[i]) for i in range(n_samples)]\n",
+    "\n",
+    "iog = [optimality_gaps[i][0][0] for i in range(n_samples)]\n",
+    "cog = [np.sum(optimality_gaps[i]) for i in range(n_samples)]\n",
+    "fog = [optimality_gaps[i][-1][0] for i in range(n_samples)]\n",
+    "\n",
+    "print(\"\\033[96m\" + f\"Initial optimality gaps: {iog}\" + \"\\033[0m\")\n",
+    "print(\"\\033[96m\" + f\"Cumulative optimality gaps: {cog}\" + \"\\033[0m\")\n",
+    "print(\"\\033[96m\" + f\"Final optimality gaps: {fog}\" + \"\\033[0m\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}