Unified pseudocost object for the regular and deterministic mode

ci/validate_wheel.sh

@@ -22,11 +22,11 @@ PYDISTCHECK_ARGS=(
 if [[ "${package_dir}" == "python/libcuopt" ]]; then
     if [[ "${RAPIDS_CUDA_MAJOR}" == "12" ]]; then
         PYDISTCHECK_ARGS+=(
-            --max-allowed-size-compressed '650Mi'
+            --max-allowed-size-compressed '660Mi'
         )
     else
         PYDISTCHECK_ARGS+=(
-            --max-allowed-size-compressed '495Mi'
+            --max-allowed-size-compressed '520Mi'
         )
     fi
 elif [[ "${package_dir}" != "python/cuopt" ]] && \

cpp/src/branch_and_bound/CMakeLists.txt

@@ -5,7 +5,6 @@
 
 set(BRANCH_AND_BOUND_SRC_FILES
   ${CMAKE_CURRENT_SOURCE_DIR}/branch_and_bound.cpp
-  ${CMAKE_CURRENT_SOURCE_DIR}/mip_node.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/pseudo_costs.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/diving_heuristics.cpp
   )

cpp/src/branch_and_bound/branch_and_bound.cpp

@@ -6,6 +6,7 @@
 /* clang-format on */
 
 #include <branch_and_bound/branch_and_bound.hpp>
+#include <branch_and_bound/diving_heuristics.hpp>
 #include <branch_and_bound/mip_node.hpp>
 #include <branch_and_bound/pseudo_costs.hpp>
 
@@ -35,15 +36,12 @@
 #include <deque>
 #include <future>
 #include <limits>
-#include <map>
 #include <optional>
 #include <string>
 #include <thread>
-#include <unordered_map>
 #include <vector>
 
 namespace cuopt::linear_programming::dual_simplex {
-
 namespace {
 
 template <typename f_t>
@@ -1039,7 +1037,9 @@ struct deterministic_bfs_policy_t
                                                 const std::vector<i_t>& fractional,
                                                 const std::vector<f_t>& x) override
   {
-    i_t var  = this->worker.pc_snapshot.variable_selection(fractional, x);
+    logger_t log;
+    log.log  = false;
+    i_t var  = this->worker.pc_snapshot.variable_selection(fractional, x, log);
     auto dir = martin_criteria(x[var], this->bnb.root_relax_soln_.x[var]);
     return {var, dir};
   }
@@ -1048,8 +1048,10 @@ struct deterministic_bfs_policy_t
                                  const std::vector<i_t>& fractional,
                                  const std::vector<f_t>& x) override
   {
+    logger_t log;
+    log.log = false;
     node->objective_estimate =
-      this->worker.pc_snapshot.obj_estimate(fractional, x, node->lower_bound);
+      this->worker.pc_snapshot.obj_estimate(fractional, x, node->lower_bound, log);
   }
 
   void on_node_completed(mip_node_t<i_t, f_t>* node,
@@ -1114,33 +1116,36 @@ struct deterministic_diving_policy_t
                                                 const std::vector<i_t>& fractional,
                                                 const std::vector<f_t>& x) override
   {
+    logger_t log;
+    log.log = false;
+
     switch (this->worker.diving_type) {
       case search_strategy_t::PSEUDOCOST_DIVING:
-        return this->worker.variable_selection_from_snapshot(fractional, x);
+        return pseudocost_diving(
+          this->worker.pc_snapshot, fractional, x, *this->worker.root_solution, log);
 
       case search_strategy_t::LINE_SEARCH_DIVING:
-        if (this->worker.root_solution) {
-          logger_t log;
-          log.log = false;
-          return line_search_diving<i_t, f_t>(fractional, x, *this->worker.root_solution, log);
-        }
-        return this->worker.variable_selection_from_snapshot(fractional, x);
+        return line_search_diving<i_t, f_t>(fractional, x, *this->worker.root_solution, log);
 
       case search_strategy_t::GUIDED_DIVING:
-        return this->worker.guided_variable_selection(fractional, x);
+        if (this->worker.incumbent_snapshot.empty()) {
+          return pseudocost_diving(
+            this->worker.pc_snapshot, fractional, x, *this->worker.root_solution, log);
+        } else {
+          return guided_diving(
+            this->worker.pc_snapshot, fractional, x, this->worker.incumbent_snapshot, log);
+        }
 
       case search_strategy_t::COEFFICIENT_DIVING: {
-        logger_t log;
-        log.log = false;
-        return coefficient_diving<i_t, f_t>(this->bnb.original_lp_,
+        return coefficient_diving<i_t, f_t>(this->worker.leaf_problem,
                                             fractional,
                                             x,
                                             this->bnb.var_up_locks_,
                                             this->bnb.var_down_locks_,
                                             log);
       }
 
-      default: return this->worker.variable_selection_from_snapshot(fractional, x);
+      default: CUOPT_LOG_ERROR("Invalid diving method!"); return {-1, rounding_direction_t::NONE};
     }
   }
 
@@ -1188,6 +1193,9 @@ std::pair<node_status_t, rounding_direction_t> branch_and_bound_t<i_t, f_t>::upd
   node_status_t status                   = node_status_t::PENDING;
   rounding_direction_t round_dir         = rounding_direction_t::NONE;
 
+  worker->recompute_basis  = true;
+  worker->recompute_bounds = true;
+
   if (lp_status == dual::status_t::DUAL_UNBOUNDED) {
     node_ptr->lower_bound = inf;
     policy.graphviz(search_tree, node_ptr, "infeasible", 0.0);
@@ -1247,6 +1255,8 @@ std::pair<node_status_t, rounding_direction_t> branch_and_bound_t<i_t, f_t>::upd
       assert(dir != rounding_direction_t::NONE);
 
       policy.update_objective_estimate(node_ptr, leaf_fractional, leaf_solution.x);
+      worker->recompute_basis  = false;
+      worker->recompute_bounds = false;
 
       logger_t log;
       log.log = false;
@@ -2506,7 +2516,7 @@ mip_status_t branch_and_bound_t<i_t, f_t>::solve(mip_solution_t<i_t, f_t>& solut
   set_uninitialized_steepest_edge_norms(original_lp_, basic_list, edge_norms_);
 
   pc_.resize(original_lp_.num_cols);
-  original_lp_.A.transpose(pc_.AT);
+  original_lp_.A.transpose(*pc_.AT);
   {
     raft::common::nvtx::range scope_sb("BB::strong_branching");
     strong_branching<i_t, f_t>(original_lp_,
@@ -3321,11 +3331,12 @@ template <typename PoolT>
 void branch_and_bound_t<i_t, f_t>::deterministic_broadcast_snapshots(
   PoolT& pool, const std::vector<f_t>& incumbent_snapshot)
 {
-  deterministic_snapshot_t<i_t, f_t> snap;
-  snap.upper_bound    = upper_bound_.load();
-  snap.total_lp_iters = exploration_stats_.total_lp_iters.load();
-  snap.incumbent      = incumbent_snapshot;
-  snap.pc_snapshot    = pc_.create_snapshot();
+  deterministic_snapshot_t<i_t, f_t> snap{
+    .upper_bound    = upper_bound_,
+    .pc_snapshot    = pc_,
+    .incumbent      = incumbent_snapshot,
+    .total_lp_iters = exploration_stats_.total_lp_iters,
+  };
 
   for (auto& worker : pool) {
     worker.set_snapshots(snap);

cpp/src/branch_and_bound/branch_and_bound.hpp

@@ -8,12 +8,12 @@
 #pragma once
 
 #include <branch_and_bound/bb_event.hpp>
-#include <branch_and_bound/branch_and_bound_worker.hpp>
 #include <branch_and_bound/deterministic_workers.hpp>
-#include <branch_and_bound/diving_heuristics.hpp>
 #include <branch_and_bound/mip_node.hpp>
 #include <branch_and_bound/node_queue.hpp>
 #include <branch_and_bound/pseudo_costs.hpp>
+#include <branch_and_bound/worker.hpp>
+#include <branch_and_bound/worker_pool.hpp>
 
 #include <cuts/cuts.hpp>
 
@@ -107,7 +107,7 @@ class branch_and_bound_t {
     }
   }
 
-  // Set a solution based on the user problem during the course of the solve
+  // Set a solution based on the user problem during solve time
   void set_new_solution(const std::vector<f_t>& solution);
 
   // This queues the solution to be processed at the correct work unit timestamp

cpp/src/branch_and_bound/constants.hpp

@@ -0,0 +1,31 @@
+/* clang-format off */
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ */
+/* clang-format on */
+
+#pragma once
+
+namespace cuopt::linear_programming::dual_simplex {
+
+constexpr int num_search_strategies = 5;
+
+// Indicate the search and variable selection algorithms used by each thread
+// in B&B (See [1]).
+//
+// [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin,
+// Berlin, 2007. doi: 10.14279/depositonce-1634.
+enum search_strategy_t : int {
+  BEST_FIRST         = 0,  // Best-First + Plunging.
+  PSEUDOCOST_DIVING  = 1,  // Pseudocost diving (9.2.5)
+  LINE_SEARCH_DIVING = 2,  // Line search diving (9.2.4)
+  GUIDED_DIVING      = 3,  // Guided diving (9.2.3).
+  COEFFICIENT_DIVING = 4   // Coefficient diving (9.2.1)
+};
+
+enum class rounding_direction_t { NONE = -1, DOWN = 0, UP = 1 };
+
+enum class branch_and_bound_mode_t { PARALLEL = 0, DETERMINISTIC = 1 };
+
+}  // namespace cuopt::linear_programming::dual_simplex

cpp/src/branch_and_bound/deterministic_workers.hpp

@@ -8,9 +8,9 @@
 #pragma once
 
 #include <branch_and_bound/bb_event.hpp>
-#include <branch_and_bound/branch_and_bound_worker.hpp>
 #include <branch_and_bound/diving_heuristics.hpp>
 #include <branch_and_bound/node_queue.hpp>
+#include <branch_and_bound/worker.hpp>
 
 #include <utilities/work_limit_context.hpp>
 
@@ -58,7 +58,7 @@ struct deterministic_snapshot_t {
   f_t upper_bound;
   pseudo_cost_snapshot_t<i_t, f_t> pc_snapshot;
   std::vector<f_t> incumbent;
-  i_t total_lp_iters;
+  int64_t total_lp_iters;
 };
 
 template <typename i_t, typename f_t, typename Derived>
@@ -74,7 +74,7 @@ class deterministic_worker_base_t : public branch_and_bound_worker_t<i_t, f_t> {
 
   // Diving-specific snapshots (ignored by BFS workers)
   std::vector<f_t> incumbent_snapshot;
-  i_t total_lp_iters_snapshot{0};
+  int64_t total_lp_iters_snapshot{0};
 
   std::vector<queued_integer_solution_t<i_t, f_t>> integer_solutions;
   int next_solution_seq{0};
@@ -90,7 +90,7 @@ class deterministic_worker_base_t : public branch_and_bound_worker_t<i_t, f_t> {
                               const std::vector<variable_type_t>& var_types,
                               const simplex_solver_settings_t<i_t, f_t>& settings,
                               const std::string& context_name)
-    : base_t(id, original_lp, Arow, var_types, settings), work_context(context_name)
+    : base_t(id, original_lp, Arow, var_types, settings), work_context(context_name), pc_snapshot(1)
   {
     work_context.deterministic = true;
   }
@@ -342,22 +342,6 @@ class deterministic_diving_worker_t
       {objective, solution, depth, this->worker_id, this->next_solution_seq++});
     ++this->total_integer_solutions;
   }
-
-  branch_variable_t<i_t> variable_selection_from_snapshot(const std::vector<i_t>& fractional,
-                                                          const std::vector<f_t>& solution) const
-  {
-    assert(root_solution != nullptr);
-    return this->pc_snapshot.pseudocost_diving(fractional, solution, *root_solution);
-  }
-
-  branch_variable_t<i_t> guided_variable_selection(const std::vector<i_t>& fractional,
-                                                   const std::vector<f_t>& solution) const
-  {
-    if (this->incumbent_snapshot.empty()) {
-      return variable_selection_from_snapshot(fractional, solution);
-    }
-    return this->pc_snapshot.guided_diving(fractional, solution, this->incumbent_snapshot);
-  }
 };
 
 template <typename i_t, typename f_t, typename WorkerT, typename Derived>