From 0cad725ceb4fe29fc95d1d9b18ce1a773e0c40a7 Mon Sep 17 00:00:00 2001 From: GiggleLiu Date: Tue, 17 Mar 2026 04:19:41 +0800 Subject: [PATCH 1/5] Add plan for #639: [Rule] Knapsack to ILP --- docs/plans/2026-03-17-knapsack-to-ilp.md | 280 +++++++++++++++++++++++ 1 file changed, 280 insertions(+) create mode 100644 docs/plans/2026-03-17-knapsack-to-ilp.md diff --git a/docs/plans/2026-03-17-knapsack-to-ilp.md b/docs/plans/2026-03-17-knapsack-to-ilp.md new file mode 100644 index 000000000..2b3d162d5 --- /dev/null +++ b/docs/plans/2026-03-17-knapsack-to-ilp.md @@ -0,0 +1,280 @@ +# Knapsack to ILP Implementation Plan + +> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task. + +**Goal:** Add a `Knapsack -> ILP` reduction with closed-loop tests, a canonical example-db fixture, and a paper entry that documents the binary ILP formulation. + +**Architecture:** Mirror the existing direct optimization-to-ILP rules: create one binary ILP variable per item, add a single capacity constraint, maximize the item values, and keep solution extraction as the identity map on the item bits. Split implementation into two batches so the paper work happens only after the Rust rule, example export, and verification data exist. + +**Tech Stack:** Rust workspace, reduction registry macros, `BruteForce` and `ILPSolver`, Typst paper, GitHub PR pipeline scripts. + +--- + +## Batch 1: Rust Implementation, Registration, and Verification + +### Task 1: Add the failing rule tests first + +**Files:** +- Create: `src/unit_tests/rules/knapsack_ilp.rs` +- Reference: `src/unit_tests/rules/knapsack_qubo.rs` +- Reference: `src/unit_tests/rules/maximumclique_ilp.rs` +- Reference: `src/rules/test_helpers.rs` + +**Step 1: Write the failing tests** + +Add tests that cover: +- `test_knapsack_to_ilp_closed_loop` using the canonical 4-item example from issue `#639` +- `test_knapsack_to_ilp_structure` asserting `num_vars == num_items`, `num_constraints == 1`, `sense == ObjectiveSense::Maximize`, objective/value coefficients, and the single `<= capacity` constraint +- `test_knapsack_to_ilp_zero_capacity` asserting the optimal extracted source solution is the all-zero selection +- `#[cfg(feature = "example-db")] test_knapsack_to_ilp_canonical_example_spec` + +Use `ILPSolver` for the closed-loop path and compare the extracted solution against the source optimum/value. Reuse `assert_optimization_round_trip_from_optimization_target` if it fits cleanly; otherwise assert validity and objective preservation directly. + +**Step 2: Run the new test target to verify it fails** + +Run: `cargo test --features ilp-solver test_knapsack_to_ilp -- --nocapture` + +Expected: FAIL because `src/rules/knapsack_ilp.rs` and its registrations do not exist yet. + +**Step 3: Commit the failing test scaffold** + +Run: +```bash +git add src/unit_tests/rules/knapsack_ilp.rs +git commit -m "test: add Knapsack to ILP coverage" +``` + +Only do this if the repo policy for the current branch allows intermediate commits during execution. + +### Task 2: Implement the reduction and register it + +**Files:** +- Create: `src/rules/knapsack_ilp.rs` +- Modify: `src/rules/mod.rs` +- Reference: `src/models/misc/knapsack.rs` +- Reference: `src/models/algebraic/ilp.rs` +- Reference: `src/rules/knapsack_qubo.rs` +- Reference: `src/rules/maximumclique_ilp.rs` + +**Step 1: Write the minimal rule implementation** + +Create `ReductionKnapsackToILP` with: +- `target: ILP` +- `num_items: usize` only if you need explicit truncation during extraction; otherwise keep extraction as `to_vec()` + +Implement `ReductionResult` with: +- `type Source = Knapsack` +- `type Target = ILP` +- `target_problem()` returning the constructed ILP +- `extract_solution()` returning the item-selection bits unchanged + +Implement: +```rust +#[reduction(overhead = { + num_vars = "num_items", + num_constraints = "1", +})] +impl ReduceTo> for Knapsack { ... } +``` + +Construct the target ILP as: +- `num_vars = self.num_items()` +- `constraints = vec![LinearConstraint::le((0..n).map(|i| (i, self.weights()[i] as f64)).collect(), self.capacity() as f64)]` +- `objective = self.values().iter().enumerate().map(|(i, &v)| (i, v as f64)).collect()` +- `sense = ObjectiveSense::Maximize` + +**Step 2: Register the rule** + +In `src/rules/mod.rs`: +- add `#[cfg(feature = "ilp-solver")] pub(crate) mod knapsack_ilp;` +- extend `canonical_rule_example_specs()` with `knapsack_ilp::canonical_rule_example_specs()` + +Place both entries in the ILP-gated section beside the other `*_ilp` rules. + +**Step 3: Run the targeted tests** + +Run: `cargo test --features ilp-solver test_knapsack_to_ilp -- --nocapture` + +Expected: PASS for the new rule tests. + +**Step 4: Commit the minimal working rule** + +Run: +```bash +git add src/rules/knapsack_ilp.rs src/rules/mod.rs src/unit_tests/rules/knapsack_ilp.rs +git commit -m "feat: add Knapsack to ILP reduction" +``` + +### Task 3: Add the canonical example and bibliography support + +**Files:** +- Modify: `src/rules/knapsack_ilp.rs` +- Modify: `docs/paper/references.bib` +- Reference: `src/rules/maximumclique_ilp.rs` +- Reference: `src/rules/knapsack_qubo.rs` + +**Step 1: Add the canonical example spec** + +Inside `src/rules/knapsack_ilp.rs`, add: +- `#[cfg(feature = "example-db")] pub(crate) fn canonical_rule_example_specs() -> Vec<...>` +- a single `RuleExampleSpec` with id `knapsack_to_ilp` +- builder using `crate::example_db::specs::direct_ilp_example::<_, bool, _>(Knapsack::new(vec![1, 3, 4, 5], vec![1, 4, 5, 7], 7), |_, _| true)` or the exact issue example instance after confirming the optimum remains `(0, 1, 1, 0)` + +The canonical example should match the issue’s 4-item tutorial instance unless a solver/export limitation forces a different but equivalent example. + +**Step 2: Add the reference entry** + +Add a BibTeX entry for Papadimitriou and Steiglitz (1982) in `docs/paper/references.bib` so the later paper theorem can cite it. + +**Step 3: Run the example-db targeted test** + +Run: `cargo test --features "ilp-solver example-db" test_knapsack_to_ilp_canonical_example_spec -- --nocapture` + +Expected: PASS, with serialized source/target instances present. + +**Step 4: Commit** + +Run: +```bash +git add src/rules/knapsack_ilp.rs docs/paper/references.bib +git commit -m "test: add Knapsack to ILP example fixture" +``` + +### Task 4: Export metadata and verify the Rust side + +**Files:** +- Modify: generated files as needed from export commands +- Reference: `docs/src/reductions/` (ignored; do not stage) + +**Step 1: Regenerate exports** + +Run: +```bash +cargo run --features "ilp-solver example-db" --example export_graph +cargo run --features "ilp-solver example-db" --example export_schemas +``` + +Inspect `git status --short` afterward and stage only tracked files that belong in the PR. + +**Step 2: Run focused verification first** + +Run: +```bash +cargo test --features "ilp-solver example-db" knapsack_ilp -- --nocapture +``` + +Expected: PASS. + +**Step 3: Run repo verification** + +Run: +```bash +make test +make clippy +``` + +Expected: PASS with `ilp-solver` support enabled by the project defaults used in CI. If `make test` is too broad during iteration, run the minimum equivalent command set before the final pass, then return here for full verification. + +**Step 4: Commit verification-driven fixes** + +Run: +```bash +git add -A +git commit -m "chore: verify Knapsack to ILP integration" +``` + +Only commit if exports or verification required tracked source changes. + +## Batch 2: Paper Entry + +### Task 5: Document the theorem in the paper + +**Files:** +- Modify: `docs/paper/reductions.typ` +- Reference: `docs/paper/reductions.typ` around `#reduction-rule("MaximumClique", "ILP")` +- Reference: `docs/paper/reductions.typ` around `#reduction-rule("Knapsack", "QUBO")` + +**Step 1: Load the generated canonical example** + +Use the exported example database in Typst, following the `Knapsack -> QUBO` worked-example pattern: +- `#let ks_ilp = load-example("Knapsack", "ILP")` +- derive counts, selected items, total weight, and total value from `ks_ilp` + +**Step 2: Add the theorem body** + +Insert a new `#reduction-rule("Knapsack", "ILP", example: true, ...)` in the ILP formulations section. The theorem should: +- cite Papadimitriou and Steiglitz +- explain the binary item variables, single capacity inequality, and maximize-value objective +- mention the exact overhead: `n` variables and `1` constraint + +**Step 3: Add the proof / construction block** + +Include: +- `_Construction._` with the full ILP formulation +- `_Correctness._` both directions, arguing feasibility and objective preservation +- `_Solution extraction._` identity on the item indicators + +**Step 4: Add the worked example** + +Walk through the 4-item example from the exported fixture: +- source weights, values, capacity +- the single ILP constraint and objective +- the optimal binary solution and its extracted knapsack witness + +State clearly that the fixture stores one canonical optimal witness even if multiple optima exist. + +**Step 5: Build the paper** + +Run: `make paper` + +Expected: PASS with the new theorem and bibliography entry. + +**Step 6: Commit the paper batch** + +Run: +```bash +git add docs/paper/reductions.typ docs/paper/references.bib +git commit -m "docs: add Knapsack to ILP theorem" +``` + +## Final Review, PR Update, and Cleanup + +### Task 6: Review, summarize deviations, and prepare the branch for push + +**Files:** +- Modify: implementation files only if review finds issues +- Remove: `docs/plans/2026-03-17-knapsack-to-ilp.md` before the final push + +**Step 1: Run review-implementation** + +Run the repo-local review skill in the worktree after the Rust and paper batches are complete. Reuse the current diff instead of re-deriving the subject manually. + +**Step 2: Fix review findings** + +Address structural gaps, semantic issues, or important quality findings. Re-run the smallest relevant verification command after each fix, then rerun `review-implementation` if needed. + +**Step 3: Create the implementation summary PR comment** + +Summarize: +- new rule file and registration +- new tests and canonical example +- bibliography/paper additions +- any deviations from the issue example or plan + +**Step 4: Remove the plan file** + +Run: +```bash +git rm docs/plans/2026-03-17-knapsack-to-ilp.md +git commit -m "chore: remove plan file after implementation" +``` + +**Step 5: Final verification before push** + +Run: +```bash +git status --short +test ! -e docs/plans/2026-03-17-knapsack-to-ilp.md +``` + +Ensure ignored generated exports under `docs/src/reductions/` are not staged. From 5c2b810ac541d3ca292d7f4cdfb47d64e1539b5d Mon Sep 17 00:00:00 2001 From: GiggleLiu Date: Tue, 17 Mar 2026 04:31:59 +0800 Subject: [PATCH 2/5] Implement #639: [Rule] Knapsack to ILP --- docs/paper/reductions.typ | 36 +++++++++++++ docs/paper/references.bib | 8 +++ src/example_db/fixtures/examples.json | 25 ++++----- src/rules/knapsack_ilp.rs | 78 +++++++++++++++++++++++++++ src/rules/mod.rs | 3 ++ src/unit_tests/rules/analysis.rs | 2 + src/unit_tests/rules/graph.rs | 26 ++++++++- src/unit_tests/rules/knapsack_ilp.rs | 71 ++++++++++++++++++++++++ 8 files changed, 236 insertions(+), 13 deletions(-) create mode 100644 src/rules/knapsack_ilp.rs create mode 100644 src/unit_tests/rules/knapsack_ilp.rs diff --git a/docs/paper/reductions.typ b/docs/paper/reductions.typ index 01372f7cb..def3fa4bd 100644 --- a/docs/paper/reductions.typ +++ b/docs/paper/reductions.typ @@ -3028,6 +3028,42 @@ The following reductions to Integer Linear Programming are straightforward formu _Solution extraction._ $K = {v : x_v = 1}$. ] +#let ks_ilp = load-example("Knapsack", "ILP") +#let ks_ilp_sol = ks_ilp.solutions.at(0) +#let ks_ilp_selected = ks_ilp_sol.source_config.enumerate().filter(((i, x)) => x == 1).map(((i, x)) => i) +#let ks_ilp_sel_weight = ks_ilp_selected.fold(0, (a, i) => a + ks_ilp.source.instance.weights.at(i)) +#let ks_ilp_sel_value = ks_ilp_selected.fold(0, (a, i) => a + ks_ilp.source.instance.values.at(i)) +#reduction-rule("Knapsack", "ILP", + example: true, + example-caption: [$n = #ks_ilp.source.instance.weights.len()$ items, capacity $C = #ks_ilp.source.instance.capacity$], + extra: [ + *Step 1 -- Source instance.* The canonical knapsack instance has weights $(#ks_ilp.source.instance.weights.map(str).join(", "))$, values $(#ks_ilp.source.instance.values.map(str).join(", "))$, and capacity $C = #ks_ilp.source.instance.capacity$. + + *Step 2 -- Build the binary ILP.* Introduce one binary variable per item: + $#range(ks_ilp.source.instance.weights.len()).map(i => $x_#i$).join(", ") in {0,1}$. + The objective is + $ max #ks_ilp.source.instance.values.enumerate().map(((i, v)) => $#v x_#i$).join($+$) $ + subject to the single capacity inequality + $ #ks_ilp.source.instance.weights.enumerate().map(((i, w)) => $#w x_#i$).join($+$) <= #ks_ilp.source.instance.capacity $. + + *Step 3 -- Verify a solution.* The ILP optimum $bold(x)^* = (#ks_ilp_sol.target_config.map(str).join(", "))$ extracts directly to the knapsack selection $bold(x)^* = (#ks_ilp_sol.source_config.map(str).join(", "))$, choosing items $\{#ks_ilp_selected.map(str).join(", ")\}$. Their total weight is $#ks_ilp_selected.map(i => str(ks_ilp.source.instance.weights.at(i))).join(" + ") = #ks_ilp_sel_weight$ and their total value is $#ks_ilp_selected.map(i => str(ks_ilp.source.instance.values.at(i))).join(" + ") = #ks_ilp_sel_value$ #sym.checkmark. + + *Uniqueness:* The fixture stores one canonical optimal witness. For this instance the optimum is unique: items $\{#ks_ilp_selected.map(str).join(", ")\}$ are the only feasible choice achieving value #ks_ilp_sel_value. + ], +)[ + A 0-1 Knapsack instance is already a binary Integer Linear Program @papadimitriou-steiglitz1982: each item-selection bit becomes a binary variable, the capacity condition is a single linear inequality, and the value objective is linear. The reduction preserves the number of decision variables exactly, producing an ILP with $n$ variables and one constraint. +][ + _Construction._ Given nonnegative weights $w_0, dots, w_(n-1)$, nonnegative values $v_0, dots, v_(n-1)$, and capacity $C$, introduce binary variables $x_0, dots, x_(n-1) in {0,1}$ where $x_i = 1$ iff item $i$ is selected. Construct the binary ILP: + $ max sum_(i=0)^(n-1) v_i x_i $ + subject to + $ sum_(i=0)^(n-1) w_i x_i <= C $ + and $x_i in {0,1}$ for all $i$. The target therefore has exactly $n$ variables and one linear constraint. + + _Correctness._ ($arrow.r.double$) Any feasible knapsack solution $bold(x)$ satisfies $sum_i w_i x_i <= C$, so the same binary vector is feasible for the ILP and attains identical objective value $sum_i v_i x_i$. ($arrow.l.double$) Any feasible binary ILP solution selects exactly the items with $x_i = 1$; the single inequality guarantees the chosen set fits in the knapsack, and the ILP objective equals the knapsack value. Therefore optimal solutions correspond one-to-one and preserve the optimum value. + + _Solution extraction._ Identity: return the binary variable vector $bold(x)$ as the knapsack selection. +] + #reduction-rule("MaximumClique", "MaximumIndependentSet", example: true, example-caption: [Path graph $P_4$: clique in $G$ maps to independent set in complement $overline(G)$.], diff --git a/docs/paper/references.bib b/docs/paper/references.bib index 6482b48a2..4cbbaafdb 100644 --- a/docs/paper/references.bib +++ b/docs/paper/references.bib @@ -703,3 +703,11 @@ @article{papadimitriou1982 year = {1982}, doi = {10.1145/322307.322309} } + +@book{papadimitriou-steiglitz1982, + author = {Christos H. Papadimitriou and Kenneth Steiglitz}, + title = {Combinatorial Optimization: Algorithms and Complexity}, + publisher = {Prentice-Hall}, + address = {Englewood Cliffs, NJ}, + year = {1982} +} diff --git a/src/example_db/fixtures/examples.json b/src/example_db/fixtures/examples.json index 6e52883b2..cc9f963be 100644 --- a/src/example_db/fixtures/examples.json +++ b/src/example_db/fixtures/examples.json @@ -50,18 +50,19 @@ {"source":{"problem":"KColoring","variant":{"graph":"SimpleGraph","k":"KN"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"num_colors":3}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Eq","rhs":1.0,"terms":[[0,1.0],[1,1.0],[2,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[3,1.0],[4,1.0],[5,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[6,1.0],[7,1.0],[8,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[9,1.0],[10,1.0],[11,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[12,1.0],[13,1.0],[14,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[15,1.0],[16,1.0],[17,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[18,1.0],[19,1.0],[20,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[21,1.0],[22,1.0],[23,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[24,1.0],[25,1.0],[26,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[27,1.0],[28,1.0],[29,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[3,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[4,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[2,1.0],[5,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[12,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[13,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[2,1.0],[14,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[15,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[16,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[2,1.0],[17,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[3,1.0],[6,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[4,1.0],[7,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[5,1.0],[8,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[3,1.0],[18,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[4,1.0],[19,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[5,1.0],[20,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[6,1.0],[9,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[7,1.0],[10,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[8,1.0],[11,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[6,1.0],[21,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[7,1.0],[22,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[8,1.0],[23,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[9,1.0],[12,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[10,1.0],[13,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[11,1.0],[14,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[9,1.0],[24,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[10,1.0],[25,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[11,1.0],[26,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[12,1.0],[27,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[13,1.0],[28,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[14,1.0],[29,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[15,1.0],[21,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[16,1.0],[22,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[17,1.0],[23,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[15,1.0],[24,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[16,1.0],[25,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[17,1.0],[26,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[18,1.0],[24,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[19,1.0],[25,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[20,1.0],[26,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[18,1.0],[27,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[19,1.0],[28,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[20,1.0],[29,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[21,1.0],[27,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[22,1.0],[28,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[23,1.0],[29,1.0]]}],"num_vars":30,"objective":[],"sense":"Minimize"}},"solutions":[{"source_config":[0,2,0,1,2,1,1,2,0,0],"target_config":[1,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,1,0,0,1,0,0]}]}, {"source":{"problem":"KColoring","variant":{"graph":"SimpleGraph","k":"KN"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[1,3,null],[2,3,null],[2,4,null],[3,4,null]],"node_holes":[],"nodes":[null,null,null,null,null]}},"num_colors":3}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-6.0,12.0,12.0,3.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.0,-6.0,12.0,0.0,3.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,-6.0,0.0,0.0,3.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,-6.0,12.0,12.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,-6.0,12.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,-6.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0,12.0,3.0,0.0,0.0,3.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0,0.0,3.0,0.0,0.0,3.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,0.0,0.0,3.0,0.0,0.0,3.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0,12.0,3.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0,0.0,3.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,0.0,0.0,3.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0,12.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0]],"num_vars":15}},"solutions":[{"source_config":[1,2,2,1,0],"target_config":[0,1,0,0,0,1,0,0,1,0,1,0,1,0,0]}]}, {"source":{"problem":"KSatisfiability","variant":{"k":"K2"},"instance":{"clauses":[{"literals":[1,2]},{"literals":[-1,3]},{"literals":[-2,4]},{"literals":[-3,-4]}],"num_vars":4}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[0.0,1.0,-1.0,0.0],[0.0,0.0,0.0,-1.0],[0.0,0.0,0.0,1.0],[0.0,0.0,0.0,0.0]],"num_vars":4}},"solutions":[{"source_config":[0,1,0,1],"target_config":[0,1,0,1]}]}, - {"source":{"problem":"KSatisfiability","variant":{"k":"K3"},"instance":{"clauses":[{"literals":[1,2,-3]},{"literals":[-1,3,4]},{"literals":[2,-4,5]},{"literals":[-2,3,-5]},{"literals":[1,-3,5]},{"literals":[-1,-2,4]},{"literals":[3,-4,-5]}],"num_vars":5}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[0.0,4.0,-4.0,0.0,0.0,4.0,-4.0,0.0,0.0,4.0,-4.0,0.0],[0.0,0.0,-2.0,-2.0,0.0,4.0,0.0,4.0,-4.0,0.0,-4.0,0.0],[0.0,0.0,2.0,-2.0,0.0,1.0,4.0,0.0,4.0,-4.0,0.0,4.0],[0.0,0.0,0.0,4.0,0.0,0.0,-1.0,-4.0,0.0,0.0,-1.0,-4.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,-1.0,1.0,-1.0,0.0,1.0],[0.0,0.0,0.0,0.0,0.0,-2.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0]],"num_vars":12}},"solutions":[{"source_config":[0,0,0,0,0],"target_config":[0,0,0,0,0,1,0,0,0,0,0,0]}]}, + {"source":{"problem":"KSatisfiability","variant":{"k":"K3"},"instance":{"clauses":[{"literals":[1,2,-3]},{"literals":[-1,3,4]},{"literals":[2,-4,5]},{"literals":[-2,3,-5]},{"literals":[1,-3,5]},{"literals":[-1,-2,4]},{"literals":[3,-4,-5]}],"num_vars":5}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[0.0,4.0,-4.0,0.0,0.0,4.0,-4.0,0.0,0.0,4.0,-4.0,0.0],[0.0,0.0,-2.0,-2.0,0.0,4.0,0.0,4.0,-4.0,0.0,-4.0,0.0],[0.0,0.0,2.0,-2.0,0.0,1.0,4.0,0.0,4.0,-4.0,0.0,4.0],[0.0,0.0,0.0,4.0,0.0,0.0,-1.0,-4.0,0.0,0.0,-1.0,-4.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,-1.0,1.0,-1.0,0.0,1.0],[0.0,0.0,0.0,0.0,0.0,-2.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0]],"num_vars":12}},"solutions":[{"source_config":[1,1,1,1,1],"target_config":[1,1,1,1,1,0,0,0,0,0,1,0]}]}, {"source":{"problem":"KSatisfiability","variant":{"k":"K3"},"instance":{"clauses":[{"literals":[1,2,3]},{"literals":[-1,-2,3]}],"num_vars":3}},"target":{"problem":"SubsetSum","variant":{},"instance":{"sizes":["10010","10001","1010","1001","111","100","10","20","1","2"],"target":"11144"}},"solutions":[{"source_config":[0,0,1],"target_config":[0,1,0,1,1,0,1,1,1,0]}]}, {"source":{"problem":"KSatisfiability","variant":{"k":"KN"},"instance":{"clauses":[{"literals":[1,-2,3]},{"literals":[-1,3,4]},{"literals":[2,-3,-4]}],"num_vars":4}},"target":{"problem":"Satisfiability","variant":{},"instance":{"clauses":[{"literals":[1,-2,3]},{"literals":[-1,3,4]},{"literals":[2,-3,-4]}],"num_vars":4}},"solutions":[{"source_config":[1,1,1,0],"target_config":[1,1,1,0]}]}, + {"source":{"problem":"Knapsack","variant":{},"instance":{"capacity":7,"values":[1,4,5,7],"weights":[1,3,4,5]}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Le","rhs":7.0,"terms":[[0,1.0],[1,3.0],[2,4.0],[3,5.0]]}],"num_vars":4,"objective":[[0,1.0],[1,4.0],[2,5.0],[3,7.0]],"sense":"Maximize"}},"solutions":[{"source_config":[0,1,1,0],"target_config":[0,1,1,0]}]}, {"source":{"problem":"Knapsack","variant":{},"instance":{"capacity":7,"values":[3,4,5,7],"weights":[2,3,4,5]}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-483.0,240.0,320.0,400.0,80.0,160.0,320.0],[0.0,-664.0,480.0,600.0,120.0,240.0,480.0],[0.0,0.0,-805.0,800.0,160.0,320.0,640.0],[0.0,0.0,0.0,-907.0,200.0,400.0,800.0],[0.0,0.0,0.0,0.0,-260.0,80.0,160.0],[0.0,0.0,0.0,0.0,0.0,-480.0,320.0],[0.0,0.0,0.0,0.0,0.0,0.0,-800.0]],"num_vars":7}},"solutions":[{"source_config":[1,0,0,1],"target_config":[1,0,0,1,0,0,0]}]}, {"source":{"problem":"LongestCommonSubsequence","variant":{},"instance":{"strings":[[65,66,65,67],[66,65,67,65]]}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[1,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[2,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[3,1.0],[4,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[5,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[2,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[3,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[5,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[4,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[2,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[2,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[3,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[5,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[4,1.0],[5,1.0]]}],"num_vars":6,"objective":[[0,1.0],[1,1.0],[2,1.0],[3,1.0],[4,1.0],[5,1.0]],"sense":"Maximize"}},"solutions":[{"source_config":[0,1,1,1],"target_config":[0,0,1,1,0,1]}]}, - {"source":{"problem":"MaxCut","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"target":{"problem":"SpinGlass","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"couplings":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],"fields":[0,0,0,0,0,0,0,0,0,0],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"solutions":[{"source_config":[0,1,0,1,0,1,0,0,0,1],"target_config":[0,1,0,1,0,1,0,0,0,1]}]}, + {"source":{"problem":"MaxCut","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"target":{"problem":"SpinGlass","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"couplings":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],"fields":[0,0,0,0,0,0,0,0,0,0],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"solutions":[{"source_config":[1,0,1,0,0,0,0,0,1,1],"target_config":[1,0,1,0,0,0,0,0,1,1]}]}, {"source":{"problem":"MaximumClique","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[0,3,null],[0,4,null],[1,2,null],[1,3,null],[1,5,null],[2,4,null],[2,5,null],[3,4,null],[3,5,null],[4,5,null]],"node_holes":[],"nodes":[null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1]}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[5,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[4,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[2,1.0],[3,1.0]]}],"num_vars":6,"objective":[[0,1.0],[1,1.0],[2,1.0],[3,1.0],[4,1.0],[5,1.0]],"sense":"Maximize"}},"solutions":[{"source_config":[1,1,1,0,0,0],"target_config":[1,1,1,0,0,0]}]}, {"source":{"problem":"MaximumClique","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[1,2,null],[2,3,null]],"node_holes":[],"nodes":[null,null,null,null]}},"weights":[1,1,1,1]}},"target":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,2,null],[0,3,null],[1,3,null]],"node_holes":[],"nodes":[null,null,null,null]}},"weights":[1,1,1,1]}},"solutions":[{"source_config":[0,1,1,0],"target_config":[0,1,1,0]}]}, - {"source":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"One"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"target":{"problem":"MaximumSetPacking","variant":{"weight":"One"},"instance":{"sets":[[0,1,2],[0,3,4],[3,5,6],[5,7,8],[1,7,9],[2,10,11],[4,12,13],[6,10,14],[8,11,12],[9,13,14]],"weights":[1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[1,0,0,1,0,0,1,1,0,0],"target_config":[1,0,0,1,0,0,1,1,0,0]}]}, + {"source":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"One"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"target":{"problem":"MaximumSetPacking","variant":{"weight":"One"},"instance":{"sets":[[0,1,2],[0,3,4],[3,5,6],[5,7,8],[1,7,9],[2,10,11],[4,12,13],[6,10,14],[8,11,12],[9,13,14]],"weights":[1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[1,0,1,0,0,0,0,0,1,1],"target_config":[1,0,1,0,0,0,0,0,1,1]}]}, {"source":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[1,2,null],[2,3,null],[3,4,null]],"node_holes":[],"nodes":[null,null,null,null,null]}},"weights":[1,1,1,1,1]}},"target":{"problem":"MaximumClique","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,2,null],[0,3,null],[0,4,null],[1,3,null],[1,4,null],[2,4,null]],"node_holes":[],"nodes":[null,null,null,null,null]}},"weights":[1,1,1,1,1]}},"solutions":[{"source_config":[1,0,1,0,1],"target_config":[1,0,1,0,1]}]}, - {"source":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"target":{"problem":"MaximumSetPacking","variant":{"weight":"i32"},"instance":{"sets":[[0,1,2],[0,3,4],[3,5,6],[5,7,8],[1,7,9],[2,10,11],[4,12,13],[6,10,14],[8,11,12],[9,13,14]],"weights":[1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[1,0,0,1,0,0,1,1,0,0],"target_config":[1,0,0,1,0,0,1,1,0,0]}]}, - {"source":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"target":{"problem":"MinimumVertexCover","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[1,0,0,1,0,0,1,1,0,0],"target_config":[0,1,1,0,1,1,0,0,1,1]}]}, + {"source":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"target":{"problem":"MaximumSetPacking","variant":{"weight":"i32"},"instance":{"sets":[[0,1,2],[0,3,4],[3,5,6],[5,7,8],[1,7,9],[2,10,11],[4,12,13],[6,10,14],[8,11,12],[9,13,14]],"weights":[1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[1,0,1,0,0,0,0,0,1,1],"target_config":[1,0,1,0,0,0,0,0,1,1]}]}, + {"source":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"target":{"problem":"MinimumVertexCover","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[1,0,1,0,0,0,0,0,1,1],"target_config":[0,1,0,1,1,1,1,1,0,0]}]}, {"source":{"problem":"MaximumMatching","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[1,1.0],[2,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[3,1.0],[4,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[3,1.0],[5,1.0],[6,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[5,1.0],[7,1.0],[8,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[7,1.0],[9,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[2,1.0],[10,1.0],[11,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[4,1.0],[12,1.0],[13,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[6,1.0],[10,1.0],[14,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[8,1.0],[11,1.0],[12,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[9,1.0],[13,1.0],[14,1.0]]}],"num_vars":15,"objective":[[0,1.0],[1,1.0],[2,1.0],[3,1.0],[4,1.0],[5,1.0],[6,1.0],[7,1.0],[8,1.0],[9,1.0],[10,1.0],[11,1.0],[12,1.0],[13,1.0],[14,1.0]],"sense":"Maximize"}},"solutions":[{"source_config":[0,0,1,1,0,0,0,1,0,0,0,0,1,0,1],"target_config":[0,0,1,1,0,0,0,1,0,0,0,0,1,0,1]}]}, {"source":{"problem":"MaximumMatching","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"target":{"problem":"MaximumSetPacking","variant":{"weight":"i32"},"instance":{"sets":[[0,1],[0,4],[0,5],[1,2],[1,6],[2,3],[2,7],[3,4],[3,8],[4,9],[5,7],[5,8],[6,8],[6,9],[7,9]],"weights":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[0,0,1,1,0,0,0,1,0,0,0,0,1,0,1],"target_config":[0,0,1,1,0,0,0,1,0,0,0,0,1,0,1]}]}, {"source":{"problem":"MaximumSetPacking","variant":{"weight":"One"},"instance":{"sets":[[0,1,2],[2,3],[4,5,6],[1,5,7],[3,6]],"weights":[1,1,1,1,1]}},"target":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"One"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,3,null],[1,4,null],[2,3,null],[2,4,null]],"node_holes":[],"nodes":[null,null,null,null,null]}},"weights":[1,1,1,1,1]}},"solutions":[{"source_config":[1,0,0,0,1],"target_config":[1,0,0,0,1]}]}, @@ -70,18 +71,18 @@ {"source":{"problem":"MaximumSetPacking","variant":{"weight":"i32"},"instance":{"sets":[[0,1,2],[2,3],[4,5,6],[1,5,7],[3,6]],"weights":[1,1,1,1,1]}},"target":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,3,null],[1,4,null],[2,3,null],[2,4,null]],"node_holes":[],"nodes":[null,null,null,null,null]}},"weights":[1,1,1,1,1]}},"solutions":[{"source_config":[1,0,0,0,1],"target_config":[1,0,0,0,1]}]}, {"source":{"problem":"MinimumDominatingSet","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Ge","rhs":1.0,"terms":[[0,1.0],[5,1.0],[4,1.0],[1,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[1,1.0],[6,1.0],[2,1.0],[0,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[2,1.0],[7,1.0],[3,1.0],[1,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[3,1.0],[8,1.0],[4,1.0],[2,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[4,1.0],[9,1.0],[3,1.0],[0,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[5,1.0],[8,1.0],[7,1.0],[0,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[6,1.0],[9,1.0],[8,1.0],[1,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[7,1.0],[9,1.0],[5,1.0],[2,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[8,1.0],[6,1.0],[5,1.0],[3,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[9,1.0],[7,1.0],[6,1.0],[4,1.0]]}],"num_vars":10,"objective":[[0,1.0],[1,1.0],[2,1.0],[3,1.0],[4,1.0],[5,1.0],[6,1.0],[7,1.0],[8,1.0],[9,1.0]],"sense":"Minimize"}},"solutions":[{"source_config":[0,0,1,0,0,1,0,0,0,1],"target_config":[0,0,1,0,0,1,0,0,0,1]}]}, {"source":{"problem":"MinimumSetCovering","variant":{"weight":"i32"},"instance":{"sets":[[0,1,2],[2,3,4],[4,5,6],[6,7,0],[1,3,5],[0,4,7]],"universe_size":8,"weights":[1,1,1,1,1,1]}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Ge","rhs":1.0,"terms":[[0,1.0],[3,1.0],[5,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[0,1.0],[4,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[0,1.0],[1,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[1,1.0],[4,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[1,1.0],[2,1.0],[5,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[2,1.0],[4,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[2,1.0],[3,1.0]]},{"cmp":"Ge","rhs":1.0,"terms":[[3,1.0],[5,1.0]]}],"num_vars":6,"objective":[[0,1.0],[1,1.0],[2,1.0],[3,1.0],[4,1.0],[5,1.0]],"sense":"Minimize"}},"solutions":[{"source_config":[0,1,0,1,1,0],"target_config":[0,1,0,1,1,0]}]}, - {"source":{"problem":"MinimumVertexCover","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"target":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[0,1,1,0,1,1,0,0,1,1],"target_config":[1,0,0,1,0,0,1,1,0,0]}]}, - {"source":{"problem":"MinimumVertexCover","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"target":{"problem":"MinimumSetCovering","variant":{"weight":"i32"},"instance":{"sets":[[0,1,2],[0,3,4],[3,5,6],[5,7,8],[1,7,9],[2,10,11],[4,12,13],[6,10,14],[8,11,12],[9,13,14]],"universe_size":15,"weights":[1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[0,1,1,0,1,1,0,0,1,1],"target_config":[0,1,1,0,1,1,0,0,1,1]}]}, + {"source":{"problem":"MinimumVertexCover","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"target":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[0,1,0,1,1,1,1,1,0,0],"target_config":[1,0,1,0,0,0,0,0,1,1]}]}, + {"source":{"problem":"MinimumVertexCover","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1]}},"target":{"problem":"MinimumSetCovering","variant":{"weight":"i32"},"instance":{"sets":[[0,1,2],[0,3,4],[3,5,6],[5,7,8],[1,7,9],[2,10,11],[4,12,13],[6,10,14],[8,11,12],[9,13,14]],"universe_size":15,"weights":[1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[0,1,0,1,1,1,1,1,0,0],"target_config":[0,1,0,1,1,1,1,1,0,0]}]}, {"source":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-2.0,1.0,0.0,0.0],[0.0,-3.0,2.0,0.0],[0.0,0.0,-1.0,-1.0],[0.0,0.0,0.0,-4.0]],"num_vars":4}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Le","rhs":0.0,"terms":[[4,1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[4,1.0],[1,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[4,1.0],[0,-1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[5,1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[5,1.0],[2,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[5,1.0],[1,-1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[6,1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[6,1.0],[3,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[6,1.0],[2,-1.0],[3,-1.0]]}],"num_vars":7,"objective":[[0,-2.0],[1,-3.0],[2,-1.0],[3,-4.0],[4,1.0],[5,2.0],[6,-1.0]],"sense":"Minimize"}},"solutions":[{"source_config":[1,1,1,1],"target_config":[1,1,1,1,1,1,1]}]}, {"source":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-1.0,2.0,0.0,0.0,-1.5,2.0,0.0,0.0,0.0,0.0],[0.0,-0.8,-1.5,0.0,0.0,0.0,2.0,0.0,0.0,0.0],[0.0,0.0,-0.6,-1.5,0.0,0.0,0.0,2.0,0.0,0.0],[0.0,0.0,0.0,-0.3999999999999999,-1.5,0.0,0.0,0.0,2.0,0.0],[0.0,0.0,0.0,0.0,-0.19999999999999996,0.0,0.0,0.0,0.0,-1.5],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,-1.5,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.20000000000000018,0.0,2.0,-1.5],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.40000000000000013,0.0,2.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6000000000000001,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8]],"num_vars":10}},"target":{"problem":"SpinGlass","variant":{"graph":"SimpleGraph","weight":"f64"},"instance":{"couplings":[0.5,-0.375,0.5,-0.375,0.5,-0.375,0.5,-0.375,0.5,-0.375,0.5,-0.375,0.5,-0.375,0.5],"fields":[0.125,0.22499999999999998,-0.55,-0.44999999999999996,-1.225,0.625,0.7250000000000001,1.7000000000000002,0.925,0.15000000000000002],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"solutions":[{"source_config":[1,0,1,1,1,0,1,0,0,1],"target_config":[1,0,1,1,1,0,1,0,0,1]}]}, {"source":{"problem":"Satisfiability","variant":{},"instance":{"clauses":[{"literals":[1,-2,3]},{"literals":[-1,2]},{"literals":[2,3]}],"num_vars":3}},"target":{"problem":"CircuitSAT","variant":{},"instance":{"circuit":{"assignments":[{"expr":{"op":{"Or":[{"op":{"Var":"x1"}},{"op":{"Not":{"op":{"Var":"x2"}}}},{"op":{"Var":"x3"}}]}},"outputs":["__clause_0"]},{"expr":{"op":{"Or":[{"op":{"Not":{"op":{"Var":"x1"}}}},{"op":{"Var":"x2"}}]}},"outputs":["__clause_1"]},{"expr":{"op":{"Or":[{"op":{"Var":"x2"}},{"op":{"Var":"x3"}}]}},"outputs":["__clause_2"]},{"expr":{"op":{"And":[{"op":{"Var":"__clause_0"}},{"op":{"Var":"__clause_1"}},{"op":{"Var":"__clause_2"}}]}},"outputs":["__out"]},{"expr":{"op":{"Const":true}},"outputs":["__out"]}]},"variables":["__clause_0","__clause_1","__clause_2","__out","x1","x2","x3"]}},"solutions":[{"source_config":[1,1,1],"target_config":[1,1,1,1,1,1,1]}]}, {"source":{"problem":"Satisfiability","variant":{},"instance":{"clauses":[{"literals":[1]},{"literals":[-3]},{"literals":[5]}],"num_vars":5}},"target":{"problem":"KColoring","variant":{"graph":"SimpleGraph","k":"K3"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[1,2,null],[3,2,null],[8,2,null],[3,8,null],[4,2,null],[9,2,null],[4,9,null],[5,2,null],[10,2,null],[5,10,null],[6,2,null],[11,2,null],[6,11,null],[7,2,null],[12,2,null],[7,12,null],[3,2,null],[3,1,null],[10,2,null],[10,1,null],[7,2,null],[7,1,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null,null,null,null]}},"num_colors":3}},"solutions":[{"source_config":[1,1,0,1,1],"target_config":[2,1,0,2,2,1,2,2,1,1,2,1,1]}]}, - {"source":{"problem":"Satisfiability","variant":{},"instance":{"clauses":[{"literals":[1]},{"literals":[2,-3]},{"literals":[-1,3,4]},{"literals":[2,-4,5]},{"literals":[1,-2,3,-5]},{"literals":[-1,2,-3,4,5]}],"num_vars":5}},"target":{"problem":"KSatisfiability","variant":{"k":"K3"},"instance":{"clauses":[{"literals":[1,6,7]},{"literals":[1,6,-7]},{"literals":[1,-6,8]},{"literals":[1,-6,-8]},{"literals":[2,-3,9]},{"literals":[2,-3,-9]},{"literals":[-1,3,4]},{"literals":[2,-4,5]},{"literals":[1,-2,10]},{"literals":[-10,3,-5]},{"literals":[-1,2,11]},{"literals":[-11,-3,12]},{"literals":[-12,4,5]}],"num_vars":12}},"solutions":[{"source_config":[1,1,1,0,1],"target_config":[1,1,1,0,1,0,0,0,0,1,1,1]}]}, + {"source":{"problem":"Satisfiability","variant":{},"instance":{"clauses":[{"literals":[1]},{"literals":[2,-3]},{"literals":[-1,3,4]},{"literals":[2,-4,5]},{"literals":[1,-2,3,-5]},{"literals":[-1,2,-3,4,5]}],"num_vars":5}},"target":{"problem":"KSatisfiability","variant":{"k":"K3"},"instance":{"clauses":[{"literals":[1,6,7]},{"literals":[1,6,-7]},{"literals":[1,-6,8]},{"literals":[1,-6,-8]},{"literals":[2,-3,9]},{"literals":[2,-3,-9]},{"literals":[-1,3,4]},{"literals":[2,-4,5]},{"literals":[1,-2,10]},{"literals":[-10,3,-5]},{"literals":[-1,2,11]},{"literals":[-11,-3,12]},{"literals":[-12,4,5]}],"num_vars":12}},"solutions":[{"source_config":[1,0,0,1,1],"target_config":[1,0,0,1,1,0,0,0,0,0,1,1]}]}, {"source":{"problem":"Satisfiability","variant":{},"instance":{"clauses":[{"literals":[1,2,-3]},{"literals":[-1,3,4]},{"literals":[2,-4,5]},{"literals":[-2,3,-5]},{"literals":[1,-3,5]},{"literals":[-1,-2,4]},{"literals":[3,-4,-5]}],"num_vars":5}},"target":{"problem":"MaximumIndependentSet","variant":{"graph":"SimpleGraph","weight":"One"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[1,2,null],[3,4,null],[3,5,null],[4,5,null],[6,7,null],[6,8,null],[7,8,null],[9,10,null],[9,11,null],[10,11,null],[12,13,null],[12,14,null],[13,14,null],[15,16,null],[15,17,null],[16,17,null],[18,19,null],[18,20,null],[19,20,null],[0,3,null],[0,15,null],[1,9,null],[1,16,null],[2,4,null],[2,10,null],[2,18,null],[3,12,null],[4,13,null],[5,7,null],[5,19,null],[6,9,null],[6,16,null],[7,17,null],[8,11,null],[8,20,null],[10,13,null],[11,14,null],[12,15,null],[13,18,null],[14,20,null],[17,19,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[1,1,1,1,0],"target_config":[1,0,0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,1,1,0,0]}]}, {"source":{"problem":"Satisfiability","variant":{},"instance":{"clauses":[{"literals":[1,2,-3]},{"literals":[-1,3,4]},{"literals":[2,-4,5]},{"literals":[-2,3,-5]},{"literals":[1,-3,5]},{"literals":[-1,-2,4]},{"literals":[3,-4,-5]}],"num_vars":5}},"target":{"problem":"MinimumDominatingSet","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[1,2,null],[3,4,null],[3,5,null],[4,5,null],[6,7,null],[6,8,null],[7,8,null],[9,10,null],[9,11,null],[10,11,null],[12,13,null],[12,14,null],[13,14,null],[0,15,null],[3,15,null],[7,15,null],[1,16,null],[6,16,null],[9,16,null],[3,17,null],[10,17,null],[12,17,null],[4,18,null],[6,18,null],[13,18,null],[0,19,null],[7,19,null],[12,19,null],[1,20,null],[4,20,null],[9,20,null],[6,21,null],[10,21,null],[13,21,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null]}},"weights":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]}},"solutions":[{"source_config":[1,0,1,1,1],"target_config":[1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0]}]}, - {"source":{"problem":"SpinGlass","variant":{"graph":"SimpleGraph","weight":"f64"},"instance":{"couplings":[1.0,-1.0,1.0,-1.0,1.0,-1.0,1.0,-1.0,1.0,-1.0,1.0,-1.0,1.0,-1.0,1.0],"fields":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-2.0,4.0,0.0,0.0,-4.0,4.0,0.0,0.0,0.0,0.0],[0.0,-2.0,-4.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0],[0.0,0.0,2.0,-4.0,0.0,0.0,0.0,4.0,0.0,0.0],[0.0,0.0,0.0,2.0,-4.0,0.0,0.0,0.0,4.0,0.0],[0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,-4.0],[0.0,0.0,0.0,0.0,0.0,-2.0,0.0,4.0,-4.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,-2.0,0.0,4.0,-4.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,0.0,4.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-2.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0]],"num_vars":10}},"solutions":[{"source_config":[1,0,1,1,1,0,1,0,0,1],"target_config":[1,0,1,1,1,0,1,0,0,1]}]}, - {"source":{"problem":"SpinGlass","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"couplings":[1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1],"fields":[0,0,0,0,0,0,0,0,0,0],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"target":{"problem":"MaxCut","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"solutions":[{"source_config":[1,0,1,1,1,0,1,0,0,1],"target_config":[1,0,1,1,1,0,1,0,0,1]}]}, - {"source":{"problem":"TravelingSalesman","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[10,15,20,35,25,30],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[0,3,null],[1,2,null],[1,3,null],[2,3,null]],"node_holes":[],"nodes":[null,null,null,null]}}}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Eq","rhs":1.0,"terms":[[0,1.0],[1,1.0],[2,1.0],[3,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[4,1.0],[5,1.0],[6,1.0],[7,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[8,1.0],[9,1.0],[10,1.0],[11,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[12,1.0],[13,1.0],[14,1.0],[15,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[0,1.0],[4,1.0],[8,1.0],[12,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[1,1.0],[5,1.0],[9,1.0],[13,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[2,1.0],[6,1.0],[10,1.0],[14,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[3,1.0],[7,1.0],[11,1.0],[15,1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[16,1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[16,1.0],[5,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[16,1.0],[0,-1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[17,1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[17,1.0],[1,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[17,1.0],[4,-1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[18,1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[18,1.0],[6,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[18,1.0],[1,-1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[19,1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[19,1.0],[2,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[19,1.0],[5,-1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[20,1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[20,1.0],[7,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[20,1.0],[2,-1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[21,1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[21,1.0],[3,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[21,1.0],[6,-1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[22,1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[22,1.0],[4,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[22,1.0],[3,-1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[23,1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[23,1.0],[0,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[23,1.0],[7,-1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[24,1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[24,1.0],[9,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[24,1.0],[0,-1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[25,1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[25,1.0],[1,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[25,1.0],[8,-1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[26,1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[26,1.0],[10,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[26,1.0],[1,-1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[27,1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[27,1.0],[2,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[27,1.0],[9,-1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[28,1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[28,1.0],[11,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[28,1.0],[2,-1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[29,1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[29,1.0],[3,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[29,1.0],[10,-1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[30,1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[30,1.0],[8,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[30,1.0],[3,-1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[31,1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[31,1.0],[0,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[31,1.0],[11,-1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[32,1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[32,1.0],[13,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[32,1.0],[0,-1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[33,1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[33,1.0],[1,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[33,1.0],[12,-1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[34,1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[34,1.0],[14,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[34,1.0],[1,-1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[35,1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[35,1.0],[2,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[35,1.0],[13,-1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[36,1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[36,1.0],[15,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[36,1.0],[2,-1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[37,1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[37,1.0],[3,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[37,1.0],[14,-1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[38,1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[38,1.0],[12,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[38,1.0],[3,-1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[39,1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[39,1.0],[0,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[39,1.0],[15,-1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[40,1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[40,1.0],[9,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[40,1.0],[4,-1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[41,1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[41,1.0],[5,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[41,1.0],[8,-1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[42,1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[42,1.0],[10,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[42,1.0],[5,-1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[43,1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[43,1.0],[6,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[43,1.0],[9,-1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[44,1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[44,1.0],[11,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[44,1.0],[6,-1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[45,1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[45,1.0],[7,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[45,1.0],[10,-1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[46,1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[46,1.0],[8,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[46,1.0],[7,-1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[47,1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[47,1.0],[4,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[47,1.0],[11,-1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[48,1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[48,1.0],[13,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[48,1.0],[4,-1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[49,1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[49,1.0],[5,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[49,1.0],[12,-1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[50,1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[50,1.0],[14,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[50,1.0],[5,-1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[51,1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[51,1.0],[6,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[51,1.0],[13,-1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[52,1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[52,1.0],[15,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[52,1.0],[6,-1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[53,1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[53,1.0],[7,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[53,1.0],[14,-1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[54,1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[54,1.0],[12,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[54,1.0],[7,-1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[55,1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[55,1.0],[4,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[55,1.0],[15,-1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[56,1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[56,1.0],[13,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[56,1.0],[8,-1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[57,1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[57,1.0],[9,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[57,1.0],[12,-1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[58,1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[58,1.0],[14,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[58,1.0],[9,-1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[59,1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[59,1.0],[10,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[59,1.0],[13,-1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[60,1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[60,1.0],[15,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[60,1.0],[10,-1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[61,1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[61,1.0],[11,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[61,1.0],[14,-1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[62,1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[62,1.0],[12,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[62,1.0],[11,-1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[63,1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[63,1.0],[8,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[63,1.0],[15,-1.0],[8,-1.0]]}],"num_vars":64,"objective":[[16,10.0],[17,10.0],[18,10.0],[19,10.0],[20,10.0],[21,10.0],[22,10.0],[23,10.0],[24,15.0],[25,15.0],[26,15.0],[27,15.0],[28,15.0],[29,15.0],[30,15.0],[31,15.0],[32,20.0],[33,20.0],[34,20.0],[35,20.0],[36,20.0],[37,20.0],[38,20.0],[39,20.0],[40,35.0],[41,35.0],[42,35.0],[43,35.0],[44,35.0],[45,35.0],[46,35.0],[47,35.0],[48,25.0],[49,25.0],[50,25.0],[51,25.0],[52,25.0],[53,25.0],[54,25.0],[55,25.0],[56,30.0],[57,30.0],[58,30.0],[59,30.0],[60,30.0],[61,30.0],[62,30.0],[63,30.0]],"sense":"Minimize"}},"solutions":[{"source_config":[1,1,0,0,1,1],"target_config":[1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0]}]}, - {"source":{"problem":"TravelingSalesman","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[1,2,3],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[1,2,null]],"node_holes":[],"nodes":[null,null,null]}}}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-14.0,14.0,14.0,14.0,1.0,1.0,14.0,2.0,2.0],[0.0,-14.0,14.0,1.0,14.0,1.0,2.0,14.0,2.0],[0.0,0.0,-14.0,1.0,1.0,14.0,2.0,2.0,14.0],[0.0,0.0,0.0,-14.0,14.0,14.0,14.0,3.0,3.0],[0.0,0.0,0.0,0.0,-14.0,14.0,3.0,14.0,3.0],[0.0,0.0,0.0,0.0,0.0,-14.0,3.0,3.0,14.0],[0.0,0.0,0.0,0.0,0.0,0.0,-14.0,14.0,14.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,-14.0,14.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-14.0]],"num_vars":9}},"solutions":[{"source_config":[1,1,1],"target_config":[0,0,1,1,0,0,0,1,0]}]} + {"source":{"problem":"SpinGlass","variant":{"graph":"SimpleGraph","weight":"f64"},"instance":{"couplings":[1.0,-1.0,1.0,-1.0,1.0,-1.0,1.0,-1.0,1.0,-1.0,1.0,-1.0,1.0,-1.0,1.0],"fields":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-2.0,4.0,0.0,0.0,-4.0,4.0,0.0,0.0,0.0,0.0],[0.0,-2.0,-4.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0],[0.0,0.0,2.0,-4.0,0.0,0.0,0.0,4.0,0.0,0.0],[0.0,0.0,0.0,2.0,-4.0,0.0,0.0,0.0,4.0,0.0],[0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,-4.0],[0.0,0.0,0.0,0.0,0.0,-2.0,0.0,4.0,-4.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,-2.0,0.0,4.0,-4.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,0.0,4.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-2.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0]],"num_vars":10}},"solutions":[{"source_config":[0,1,1,0,0,1,0,0,1,0],"target_config":[0,1,1,0,0,1,0,0,1,0]}]}, + {"source":{"problem":"SpinGlass","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"couplings":[1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1],"fields":[0,0,0,0,0,0,0,0,0,0],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"target":{"problem":"MaxCut","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"solutions":[{"source_config":[0,1,1,0,0,1,0,0,1,0],"target_config":[0,1,1,0,0,1,0,0,1,0]}]}, + {"source":{"problem":"TravelingSalesman","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[10,15,20,35,25,30],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[0,3,null],[1,2,null],[1,3,null],[2,3,null]],"node_holes":[],"nodes":[null,null,null,null]}}}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Eq","rhs":1.0,"terms":[[0,1.0],[1,1.0],[2,1.0],[3,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[4,1.0],[5,1.0],[6,1.0],[7,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[8,1.0],[9,1.0],[10,1.0],[11,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[12,1.0],[13,1.0],[14,1.0],[15,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[0,1.0],[4,1.0],[8,1.0],[12,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[1,1.0],[5,1.0],[9,1.0],[13,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[2,1.0],[6,1.0],[10,1.0],[14,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[3,1.0],[7,1.0],[11,1.0],[15,1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[16,1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[16,1.0],[5,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[16,1.0],[0,-1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[17,1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[17,1.0],[1,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[17,1.0],[4,-1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[18,1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[18,1.0],[6,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[18,1.0],[1,-1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[19,1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[19,1.0],[2,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[19,1.0],[5,-1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[20,1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[20,1.0],[7,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[20,1.0],[2,-1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[21,1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[21,1.0],[3,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[21,1.0],[6,-1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[22,1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[22,1.0],[4,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[22,1.0],[3,-1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[23,1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[23,1.0],[0,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[23,1.0],[7,-1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[24,1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[24,1.0],[9,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[24,1.0],[0,-1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[25,1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[25,1.0],[1,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[25,1.0],[8,-1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[26,1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[26,1.0],[10,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[26,1.0],[1,-1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[27,1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[27,1.0],[2,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[27,1.0],[9,-1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[28,1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[28,1.0],[11,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[28,1.0],[2,-1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[29,1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[29,1.0],[3,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[29,1.0],[10,-1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[30,1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[30,1.0],[8,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[30,1.0],[3,-1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[31,1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[31,1.0],[0,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[31,1.0],[11,-1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[32,1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[32,1.0],[13,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[32,1.0],[0,-1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[33,1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[33,1.0],[1,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[33,1.0],[12,-1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[34,1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[34,1.0],[14,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[34,1.0],[1,-1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[35,1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[35,1.0],[2,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[35,1.0],[13,-1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[36,1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[36,1.0],[15,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[36,1.0],[2,-1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[37,1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[37,1.0],[3,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[37,1.0],[14,-1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[38,1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[38,1.0],[12,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[38,1.0],[3,-1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[39,1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[39,1.0],[0,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[39,1.0],[15,-1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[40,1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[40,1.0],[9,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[40,1.0],[4,-1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[41,1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[41,1.0],[5,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[41,1.0],[8,-1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[42,1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[42,1.0],[10,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[42,1.0],[5,-1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[43,1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[43,1.0],[6,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[43,1.0],[9,-1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[44,1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[44,1.0],[11,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[44,1.0],[6,-1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[45,1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[45,1.0],[7,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[45,1.0],[10,-1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[46,1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[46,1.0],[8,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[46,1.0],[7,-1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[47,1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[47,1.0],[4,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[47,1.0],[11,-1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[48,1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[48,1.0],[13,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[48,1.0],[4,-1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[49,1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[49,1.0],[5,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[49,1.0],[12,-1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[50,1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[50,1.0],[14,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[50,1.0],[5,-1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[51,1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[51,1.0],[6,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[51,1.0],[13,-1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[52,1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[52,1.0],[15,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[52,1.0],[6,-1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[53,1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[53,1.0],[7,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[53,1.0],[14,-1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[54,1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[54,1.0],[12,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[54,1.0],[7,-1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[55,1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[55,1.0],[4,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[55,1.0],[15,-1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[56,1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[56,1.0],[13,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[56,1.0],[8,-1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[57,1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[57,1.0],[9,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[57,1.0],[12,-1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[58,1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[58,1.0],[14,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[58,1.0],[9,-1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[59,1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[59,1.0],[10,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[59,1.0],[13,-1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[60,1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[60,1.0],[15,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[60,1.0],[10,-1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[61,1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[61,1.0],[11,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[61,1.0],[14,-1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[62,1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[62,1.0],[12,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[62,1.0],[11,-1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[63,1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[63,1.0],[8,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[63,1.0],[15,-1.0],[8,-1.0]]}],"num_vars":64,"objective":[[16,10.0],[17,10.0],[18,10.0],[19,10.0],[20,10.0],[21,10.0],[22,10.0],[23,10.0],[24,15.0],[25,15.0],[26,15.0],[27,15.0],[28,15.0],[29,15.0],[30,15.0],[31,15.0],[32,20.0],[33,20.0],[34,20.0],[35,20.0],[36,20.0],[37,20.0],[38,20.0],[39,20.0],[40,35.0],[41,35.0],[42,35.0],[43,35.0],[44,35.0],[45,35.0],[46,35.0],[47,35.0],[48,25.0],[49,25.0],[50,25.0],[51,25.0],[52,25.0],[53,25.0],[54,25.0],[55,25.0],[56,30.0],[57,30.0],[58,30.0],[59,30.0],[60,30.0],[61,30.0],[62,30.0],[63,30.0]],"sense":"Minimize"}},"solutions":[{"source_config":[1,1,0,0,1,1],"target_config":[0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0]}]}, + {"source":{"problem":"TravelingSalesman","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[1,2,3],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[1,2,null]],"node_holes":[],"nodes":[null,null,null]}}}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-14.0,14.0,14.0,14.0,1.0,1.0,14.0,2.0,2.0],[0.0,-14.0,14.0,1.0,14.0,1.0,2.0,14.0,2.0],[0.0,0.0,-14.0,1.0,1.0,14.0,2.0,2.0,14.0],[0.0,0.0,0.0,-14.0,14.0,14.0,14.0,3.0,3.0],[0.0,0.0,0.0,0.0,-14.0,14.0,3.0,14.0,3.0],[0.0,0.0,0.0,0.0,0.0,-14.0,3.0,3.0,14.0],[0.0,0.0,0.0,0.0,0.0,0.0,-14.0,14.0,14.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,-14.0,14.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-14.0]],"num_vars":9}},"solutions":[{"source_config":[1,1,1],"target_config":[0,0,1,0,1,0,1,0,0]}]} ] } diff --git a/src/rules/knapsack_ilp.rs b/src/rules/knapsack_ilp.rs new file mode 100644 index 000000000..537b238cb --- /dev/null +++ b/src/rules/knapsack_ilp.rs @@ -0,0 +1,78 @@ +//! Reduction from Knapsack to ILP (Integer Linear Programming). +//! +//! The standard 0-1 knapsack formulation is already a binary ILP: +//! - Variables: one binary variable per item +//! - Constraint: the total selected weight must not exceed capacity +//! - Objective: maximize the total selected value + +use crate::models::algebraic::{ILP, LinearConstraint, ObjectiveSense}; +use crate::models::misc::Knapsack; +use crate::reduction; +use crate::rules::traits::{ReduceTo, ReductionResult}; + +/// Result of reducing Knapsack to ILP. +#[derive(Debug, Clone)] +pub struct ReductionKnapsackToILP { + target: ILP, +} + +impl ReductionResult for ReductionKnapsackToILP { + type Source = Knapsack; + type Target = ILP; + + fn target_problem(&self) -> &ILP { + &self.target + } + + fn extract_solution(&self, target_solution: &[usize]) -> Vec { + target_solution.to_vec() + } +} + +#[reduction( + overhead = { + num_vars = "num_items", + num_constraints = "1", + } +)] +impl ReduceTo> for Knapsack { + type Result = ReductionKnapsackToILP; + + fn reduce_to(&self) -> Self::Result { + let num_vars = self.num_items(); + let constraints = vec![LinearConstraint::le( + self.weights() + .iter() + .enumerate() + .map(|(i, &weight)| (i, weight as f64)) + .collect(), + self.capacity() as f64, + )]; + let objective = self + .values() + .iter() + .enumerate() + .map(|(i, &value)| (i, value as f64)) + .collect(); + let target = ILP::new(num_vars, constraints, objective, ObjectiveSense::Maximize); + + ReductionKnapsackToILP { target } + } +} + +#[cfg(feature = "example-db")] +pub(crate) fn canonical_rule_example_specs() -> Vec { + vec![crate::example_db::specs::RuleExampleSpec { + id: "knapsack_to_ilp", + build: || { + crate::example_db::specs::direct_ilp_example::<_, bool, _>( + Knapsack::new(vec![1, 3, 4, 5], vec![1, 4, 5, 7], 7), + |_, _| true, + ) + }, + }] +} + +#[cfg(test)] +#[path = "../unit_tests/rules/knapsack_ilp.rs"] +mod tests; diff --git a/src/rules/mod.rs b/src/rules/mod.rs index 6fc0ae7a0..8416e33bf 100644 --- a/src/rules/mod.rs +++ b/src/rules/mod.rs @@ -54,6 +54,8 @@ mod ilp_bool_ilp_i32; #[cfg(feature = "ilp-solver")] pub(crate) mod ilp_qubo; #[cfg(feature = "ilp-solver")] +pub(crate) mod knapsack_ilp; +#[cfg(feature = "ilp-solver")] pub(crate) mod longestcommonsubsequence_ilp; #[cfg(feature = "ilp-solver")] pub(crate) mod maximumclique_ilp; @@ -107,6 +109,7 @@ pub(crate) fn canonical_rule_example_specs() -> Vec ILP -> QUBO is better than the direct penalty reduction + ("Knapsack", "QUBO {weight: \"f64\"}"), // MaxMatching → MaxSetPacking → ILP is better than direct MaxMatching → ILP ( "MaximumMatching {graph: \"SimpleGraph\", weight: \"i32\"}", diff --git a/src/unit_tests/rules/graph.rs b/src/unit_tests/rules/graph.rs index 1c6a1a8be..a7c5d9326 100644 --- a/src/unit_tests/rules/graph.rs +++ b/src/unit_tests/rules/graph.rs @@ -1,6 +1,7 @@ use super::*; -use crate::models::algebraic::QUBO; +use crate::models::algebraic::{ILP, QUBO}; use crate::models::graph::{MaximumIndependentSet, MinimumVertexCover}; +use crate::models::misc::Knapsack; use crate::models::set::MaximumSetPacking; use crate::rules::cost::{Minimize, MinimizeSteps}; use crate::rules::graph::{classify_problem_category, ReductionStep}; @@ -50,6 +51,29 @@ fn test_find_shortest_path() { assert_eq!(path.len(), 1); // Direct path exists } +#[test] +fn test_knapsack_to_ilp_path_exists() { + let graph = ReductionGraph::new(); + let src = ReductionGraph::variant_to_map(&Knapsack::variant()); + let dst = ReductionGraph::variant_to_map(&ILP::::variant()); + let path = graph.find_cheapest_path( + "Knapsack", + &src, + "ILP", + &dst, + &ProblemSize::new(vec![]), + &MinimizeSteps, + ); + + let path = path.expect("Knapsack should reduce to ILP"); + assert_eq!( + path.type_names(), + vec!["Knapsack", "ILP"], + "Knapsack should have a direct ILP reduction" + ); + assert_eq!(path.len(), 1, "Knapsack -> ILP should be one direct step"); +} + #[test] fn test_has_direct_reduction() { let graph = ReductionGraph::new(); diff --git a/src/unit_tests/rules/knapsack_ilp.rs b/src/unit_tests/rules/knapsack_ilp.rs new file mode 100644 index 000000000..30f0906fa --- /dev/null +++ b/src/unit_tests/rules/knapsack_ilp.rs @@ -0,0 +1,71 @@ +use super::*; +use crate::models::algebraic::{Comparison, ObjectiveSense, ILP}; +use crate::rules::test_helpers::assert_optimization_round_trip_from_optimization_target; +use crate::solvers::ILPSolver; + +#[test] +fn test_knapsack_to_ilp_closed_loop() { + let knapsack = Knapsack::new(vec![1, 3, 4, 5], vec![1, 4, 5, 7], 7); + let reduction = ReduceTo::>::reduce_to(&knapsack); + + assert_optimization_round_trip_from_optimization_target( + &knapsack, + &reduction, + "Knapsack->ILP closed loop", + ); + + let ilp_solution = ILPSolver::new() + .solve(reduction.target_problem()) + .expect("ILP should be solvable"); + let extracted = reduction.extract_solution(&ilp_solution); + assert_eq!(extracted, vec![0, 1, 1, 0]); +} + +#[test] +fn test_knapsack_to_ilp_structure() { + let knapsack = Knapsack::new(vec![1, 3, 4, 5], vec![1, 4, 5, 7], 7); + let reduction = ReduceTo::>::reduce_to(&knapsack); + let ilp = reduction.target_problem(); + + assert_eq!(ilp.num_vars(), 4); + assert_eq!(ilp.num_constraints(), 1); + assert_eq!(ilp.sense, ObjectiveSense::Maximize); + assert_eq!(ilp.objective, vec![(0, 1.0), (1, 4.0), (2, 5.0), (3, 7.0)]); + + let constraint = &ilp.constraints[0]; + assert_eq!(constraint.cmp, Comparison::Le); + assert_eq!(constraint.rhs, 7.0); + assert_eq!( + constraint.terms, + vec![(0, 1.0), (1, 3.0), (2, 4.0), (3, 5.0)] + ); +} + +#[test] +fn test_knapsack_to_ilp_zero_capacity() { + let knapsack = Knapsack::new(vec![2, 3], vec![5, 7], 0); + let reduction = ReduceTo::>::reduce_to(&knapsack); + + let ilp_solution = ILPSolver::new() + .solve(reduction.target_problem()) + .expect("zero-capacity ILP should still be solvable"); + let extracted = reduction.extract_solution(&ilp_solution); + assert_eq!(extracted, vec![0, 0]); +} + +#[cfg(feature = "example-db")] +#[test] +fn test_knapsack_to_ilp_canonical_example_spec() { + let spec = canonical_rule_example_specs() + .into_iter() + .find(|spec| spec.id == "knapsack_to_ilp") + .expect("missing canonical Knapsack -> ILP example spec"); + let example = (spec.build)(); + + assert_eq!(example.source.problem, "Knapsack"); + assert_eq!(example.target.problem, "ILP"); + assert_eq!(example.source.instance["capacity"], 7); + assert_eq!(example.target.instance["num_vars"], 4); + assert_eq!(example.target.instance["constraints"].as_array().unwrap().len(), 1); + assert!(!example.solutions.is_empty()); +} From a457a668c50e24121478a34b5337ce923805debc Mon Sep 17 00:00:00 2001 From: GiggleLiu Date: Tue, 17 Mar 2026 04:40:24 +0800 Subject: [PATCH 3/5] Add extra tests for #639 Knapsack -> ILP --- src/unit_tests/rules/knapsack_ilp.rs | 28 +++++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/src/unit_tests/rules/knapsack_ilp.rs b/src/unit_tests/rules/knapsack_ilp.rs index 30f0906fa..0b5cfe014 100644 --- a/src/unit_tests/rules/knapsack_ilp.rs +++ b/src/unit_tests/rules/knapsack_ilp.rs @@ -53,6 +53,26 @@ fn test_knapsack_to_ilp_zero_capacity() { assert_eq!(extracted, vec![0, 0]); } +#[test] +fn test_knapsack_to_ilp_empty_instance() { + let knapsack = Knapsack::new(vec![], vec![], 0); + let reduction = ReduceTo::>::reduce_to(&knapsack); + let ilp = reduction.target_problem(); + + assert_eq!(ilp.num_vars(), 0); + assert_eq!(ilp.num_constraints(), 1); + assert_eq!(ilp.constraints[0].cmp, Comparison::Le); + assert_eq!(ilp.constraints[0].rhs, 0.0); + assert!(ilp.constraints[0].terms.is_empty()); + assert!(ilp.objective.is_empty()); + + let ilp_solution = ILPSolver::new() + .solve(ilp) + .expect("empty Knapsack ILP should still be solvable"); + let extracted = reduction.extract_solution(&ilp_solution); + assert_eq!(extracted, Vec::::new()); +} + #[cfg(feature = "example-db")] #[test] fn test_knapsack_to_ilp_canonical_example_spec() { @@ -67,5 +87,11 @@ fn test_knapsack_to_ilp_canonical_example_spec() { assert_eq!(example.source.instance["capacity"], 7); assert_eq!(example.target.instance["num_vars"], 4); assert_eq!(example.target.instance["constraints"].as_array().unwrap().len(), 1); - assert!(!example.solutions.is_empty()); + assert_eq!( + example.solutions, + vec![crate::export::SolutionPair { + source_config: vec![0, 1, 1, 0], + target_config: vec![0, 1, 1, 0], + }] + ); } From e83ab1f9e17be7b13004fac77d5967718b693182 Mon Sep 17 00:00:00 2001 From: GiggleLiu Date: Tue, 17 Mar 2026 04:40:37 +0800 Subject: [PATCH 4/5] chore: remove plan file after implementation --- docs/plans/2026-03-17-knapsack-to-ilp.md | 280 ----------------------- 1 file changed, 280 deletions(-) delete mode 100644 docs/plans/2026-03-17-knapsack-to-ilp.md diff --git a/docs/plans/2026-03-17-knapsack-to-ilp.md b/docs/plans/2026-03-17-knapsack-to-ilp.md deleted file mode 100644 index 2b3d162d5..000000000 --- a/docs/plans/2026-03-17-knapsack-to-ilp.md +++ /dev/null @@ -1,280 +0,0 @@ -# Knapsack to ILP Implementation Plan - -> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task. - -**Goal:** Add a `Knapsack -> ILP` reduction with closed-loop tests, a canonical example-db fixture, and a paper entry that documents the binary ILP formulation. - -**Architecture:** Mirror the existing direct optimization-to-ILP rules: create one binary ILP variable per item, add a single capacity constraint, maximize the item values, and keep solution extraction as the identity map on the item bits. Split implementation into two batches so the paper work happens only after the Rust rule, example export, and verification data exist. - -**Tech Stack:** Rust workspace, reduction registry macros, `BruteForce` and `ILPSolver`, Typst paper, GitHub PR pipeline scripts. - ---- - -## Batch 1: Rust Implementation, Registration, and Verification - -### Task 1: Add the failing rule tests first - -**Files:** -- Create: `src/unit_tests/rules/knapsack_ilp.rs` -- Reference: `src/unit_tests/rules/knapsack_qubo.rs` -- Reference: `src/unit_tests/rules/maximumclique_ilp.rs` -- Reference: `src/rules/test_helpers.rs` - -**Step 1: Write the failing tests** - -Add tests that cover: -- `test_knapsack_to_ilp_closed_loop` using the canonical 4-item example from issue `#639` -- `test_knapsack_to_ilp_structure` asserting `num_vars == num_items`, `num_constraints == 1`, `sense == ObjectiveSense::Maximize`, objective/value coefficients, and the single `<= capacity` constraint -- `test_knapsack_to_ilp_zero_capacity` asserting the optimal extracted source solution is the all-zero selection -- `#[cfg(feature = "example-db")] test_knapsack_to_ilp_canonical_example_spec` - -Use `ILPSolver` for the closed-loop path and compare the extracted solution against the source optimum/value. Reuse `assert_optimization_round_trip_from_optimization_target` if it fits cleanly; otherwise assert validity and objective preservation directly. - -**Step 2: Run the new test target to verify it fails** - -Run: `cargo test --features ilp-solver test_knapsack_to_ilp -- --nocapture` - -Expected: FAIL because `src/rules/knapsack_ilp.rs` and its registrations do not exist yet. - -**Step 3: Commit the failing test scaffold** - -Run: -```bash -git add src/unit_tests/rules/knapsack_ilp.rs -git commit -m "test: add Knapsack to ILP coverage" -``` - -Only do this if the repo policy for the current branch allows intermediate commits during execution. - -### Task 2: Implement the reduction and register it - -**Files:** -- Create: `src/rules/knapsack_ilp.rs` -- Modify: `src/rules/mod.rs` -- Reference: `src/models/misc/knapsack.rs` -- Reference: `src/models/algebraic/ilp.rs` -- Reference: `src/rules/knapsack_qubo.rs` -- Reference: `src/rules/maximumclique_ilp.rs` - -**Step 1: Write the minimal rule implementation** - -Create `ReductionKnapsackToILP` with: -- `target: ILP` -- `num_items: usize` only if you need explicit truncation during extraction; otherwise keep extraction as `to_vec()` - -Implement `ReductionResult` with: -- `type Source = Knapsack` -- `type Target = ILP` -- `target_problem()` returning the constructed ILP -- `extract_solution()` returning the item-selection bits unchanged - -Implement: -```rust -#[reduction(overhead = { - num_vars = "num_items", - num_constraints = "1", -})] -impl ReduceTo> for Knapsack { ... } -``` - -Construct the target ILP as: -- `num_vars = self.num_items()` -- `constraints = vec![LinearConstraint::le((0..n).map(|i| (i, self.weights()[i] as f64)).collect(), self.capacity() as f64)]` -- `objective = self.values().iter().enumerate().map(|(i, &v)| (i, v as f64)).collect()` -- `sense = ObjectiveSense::Maximize` - -**Step 2: Register the rule** - -In `src/rules/mod.rs`: -- add `#[cfg(feature = "ilp-solver")] pub(crate) mod knapsack_ilp;` -- extend `canonical_rule_example_specs()` with `knapsack_ilp::canonical_rule_example_specs()` - -Place both entries in the ILP-gated section beside the other `*_ilp` rules. - -**Step 3: Run the targeted tests** - -Run: `cargo test --features ilp-solver test_knapsack_to_ilp -- --nocapture` - -Expected: PASS for the new rule tests. - -**Step 4: Commit the minimal working rule** - -Run: -```bash -git add src/rules/knapsack_ilp.rs src/rules/mod.rs src/unit_tests/rules/knapsack_ilp.rs -git commit -m "feat: add Knapsack to ILP reduction" -``` - -### Task 3: Add the canonical example and bibliography support - -**Files:** -- Modify: `src/rules/knapsack_ilp.rs` -- Modify: `docs/paper/references.bib` -- Reference: `src/rules/maximumclique_ilp.rs` -- Reference: `src/rules/knapsack_qubo.rs` - -**Step 1: Add the canonical example spec** - -Inside `src/rules/knapsack_ilp.rs`, add: -- `#[cfg(feature = "example-db")] pub(crate) fn canonical_rule_example_specs() -> Vec<...>` -- a single `RuleExampleSpec` with id `knapsack_to_ilp` -- builder using `crate::example_db::specs::direct_ilp_example::<_, bool, _>(Knapsack::new(vec![1, 3, 4, 5], vec![1, 4, 5, 7], 7), |_, _| true)` or the exact issue example instance after confirming the optimum remains `(0, 1, 1, 0)` - -The canonical example should match the issue’s 4-item tutorial instance unless a solver/export limitation forces a different but equivalent example. - -**Step 2: Add the reference entry** - -Add a BibTeX entry for Papadimitriou and Steiglitz (1982) in `docs/paper/references.bib` so the later paper theorem can cite it. - -**Step 3: Run the example-db targeted test** - -Run: `cargo test --features "ilp-solver example-db" test_knapsack_to_ilp_canonical_example_spec -- --nocapture` - -Expected: PASS, with serialized source/target instances present. - -**Step 4: Commit** - -Run: -```bash -git add src/rules/knapsack_ilp.rs docs/paper/references.bib -git commit -m "test: add Knapsack to ILP example fixture" -``` - -### Task 4: Export metadata and verify the Rust side - -**Files:** -- Modify: generated files as needed from export commands -- Reference: `docs/src/reductions/` (ignored; do not stage) - -**Step 1: Regenerate exports** - -Run: -```bash -cargo run --features "ilp-solver example-db" --example export_graph -cargo run --features "ilp-solver example-db" --example export_schemas -``` - -Inspect `git status --short` afterward and stage only tracked files that belong in the PR. - -**Step 2: Run focused verification first** - -Run: -```bash -cargo test --features "ilp-solver example-db" knapsack_ilp -- --nocapture -``` - -Expected: PASS. - -**Step 3: Run repo verification** - -Run: -```bash -make test -make clippy -``` - -Expected: PASS with `ilp-solver` support enabled by the project defaults used in CI. If `make test` is too broad during iteration, run the minimum equivalent command set before the final pass, then return here for full verification. - -**Step 4: Commit verification-driven fixes** - -Run: -```bash -git add -A -git commit -m "chore: verify Knapsack to ILP integration" -``` - -Only commit if exports or verification required tracked source changes. - -## Batch 2: Paper Entry - -### Task 5: Document the theorem in the paper - -**Files:** -- Modify: `docs/paper/reductions.typ` -- Reference: `docs/paper/reductions.typ` around `#reduction-rule("MaximumClique", "ILP")` -- Reference: `docs/paper/reductions.typ` around `#reduction-rule("Knapsack", "QUBO")` - -**Step 1: Load the generated canonical example** - -Use the exported example database in Typst, following the `Knapsack -> QUBO` worked-example pattern: -- `#let ks_ilp = load-example("Knapsack", "ILP")` -- derive counts, selected items, total weight, and total value from `ks_ilp` - -**Step 2: Add the theorem body** - -Insert a new `#reduction-rule("Knapsack", "ILP", example: true, ...)` in the ILP formulations section. The theorem should: -- cite Papadimitriou and Steiglitz -- explain the binary item variables, single capacity inequality, and maximize-value objective -- mention the exact overhead: `n` variables and `1` constraint - -**Step 3: Add the proof / construction block** - -Include: -- `_Construction._` with the full ILP formulation -- `_Correctness._` both directions, arguing feasibility and objective preservation -- `_Solution extraction._` identity on the item indicators - -**Step 4: Add the worked example** - -Walk through the 4-item example from the exported fixture: -- source weights, values, capacity -- the single ILP constraint and objective -- the optimal binary solution and its extracted knapsack witness - -State clearly that the fixture stores one canonical optimal witness even if multiple optima exist. - -**Step 5: Build the paper** - -Run: `make paper` - -Expected: PASS with the new theorem and bibliography entry. - -**Step 6: Commit the paper batch** - -Run: -```bash -git add docs/paper/reductions.typ docs/paper/references.bib -git commit -m "docs: add Knapsack to ILP theorem" -``` - -## Final Review, PR Update, and Cleanup - -### Task 6: Review, summarize deviations, and prepare the branch for push - -**Files:** -- Modify: implementation files only if review finds issues -- Remove: `docs/plans/2026-03-17-knapsack-to-ilp.md` before the final push - -**Step 1: Run review-implementation** - -Run the repo-local review skill in the worktree after the Rust and paper batches are complete. Reuse the current diff instead of re-deriving the subject manually. - -**Step 2: Fix review findings** - -Address structural gaps, semantic issues, or important quality findings. Re-run the smallest relevant verification command after each fix, then rerun `review-implementation` if needed. - -**Step 3: Create the implementation summary PR comment** - -Summarize: -- new rule file and registration -- new tests and canonical example -- bibliography/paper additions -- any deviations from the issue example or plan - -**Step 4: Remove the plan file** - -Run: -```bash -git rm docs/plans/2026-03-17-knapsack-to-ilp.md -git commit -m "chore: remove plan file after implementation" -``` - -**Step 5: Final verification before push** - -Run: -```bash -git status --short -test ! -e docs/plans/2026-03-17-knapsack-to-ilp.md -``` - -Ensure ignored generated exports under `docs/src/reductions/` are not staged. From 91345cf3ade55d805f4fdbcba294953a84e6f689 Mon Sep 17 00:00:00 2001 From: Xiwei Pan Date: Fri, 20 Mar 2026 14:12:25 +0800 Subject: [PATCH 5/5] Fix canonical example to use rule_example_with_witness API The direct_ilp_example helper was removed on main. Update to use the current rule_example_with_witness API with an explicit SolutionPair. --- src/rules/knapsack_ilp.rs | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/src/rules/knapsack_ilp.rs b/src/rules/knapsack_ilp.rs index 537b238cb..3434d94f2 100644 --- a/src/rules/knapsack_ilp.rs +++ b/src/rules/knapsack_ilp.rs @@ -62,12 +62,17 @@ impl ReduceTo> for Knapsack { #[cfg(feature = "example-db")] pub(crate) fn canonical_rule_example_specs() -> Vec { + use crate::export::SolutionPair; + vec![crate::example_db::specs::RuleExampleSpec { id: "knapsack_to_ilp", build: || { - crate::example_db::specs::direct_ilp_example::<_, bool, _>( + crate::example_db::specs::rule_example_with_witness::<_, ILP>( Knapsack::new(vec![1, 3, 4, 5], vec![1, 4, 5, 7], 7), - |_, _| true, + SolutionPair { + source_config: vec![0, 1, 1, 0], + target_config: vec![0, 1, 1, 0], + }, ) }, }]