Batch-add 13 Backlog Models + 26 Backlog Rules#1067
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- New skill .claude/skills/auto-pipeline: orchestrator that drives one Backlog issue from quality gate to Final review via fresh-context subagents (check-issue, fix-issue, run-pipeline, review-pipeline). Substantive issue-body problems are routed to codex xhigh; fundamental flaws with no public reference park the issue on OnHold. - check-issue: add Rule Check 5 (Completeness, fail label "Incomplete"). Mandatory literature research + codebase corner-case enumeration + hand-tracing on >= 2 non-canonical instances for every [Rule] issue. - review-structural: add Step 4b (Round-trip Execution, mandatory for Rule reviews). Reviewer must run cargo test by name, paste the "test result: ok" line, and confirm the test exercises the four phases of a real round-trip. - review-quality: promote "closed-loop without round-trip verification" from a Minor flag to Critical, with explicit red flags (is_some-only, target-side-only asserts, unique-optimum instances). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Robotics inverse-kinematics problem: given link lengths l_j, target
g in R^2, per-link sampled orientations Phi_j, and consecutive-pair
admissibility sets A_j, pick indices a_j in {0..m_j-1} with
(a_{j-1}, a_j) in A_j minimizing the squared end-effector distance
||sum_j l_j (cos phi_{j,a_j}, sin phi_{j,a_j}) - g||^2.
- src/models/misc/minimum_discrete_planar_inverse_kinematics.rs:
per-link dims (non-binary), Min<f64> objective, A_j feasibility
returns Min(None), declare_variants! default entry, ProblemSchemaEntry
+ ProblemSizeFieldEntry, canonical example_db spec via inventory.
- src/unit_tests/models/misc/...: creation, evaluate (feasible/
infeasible), brute-force solver, serialization roundtrip.
- problemreductions-cli/: new (f64,f64) and Vec<Vec<(usize,usize)>>
schema parsers; --link-lengths/--target-point/--orientation-samples/
--allowed-pairs flags via the schema-driven create path.
- docs/paper: problem-def block + display-name + worked example;
references.bib entries for Salloum2025 and DaiIzattTedrake2019.
Reference: Salloum et al., "Quantum annealing for inverse kinematics
in robotics", Scientific Reports 2025, doi:10.1038/s41598-025-34346-z.
Closes #994
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The brute-force search space is prod_{j=1}^n m_j, not 2^n — per-link
sample counts m_j are arbitrary. Add a `total_configurations()` getter
that returns the product, and rewrite the declare_variants! complexity
as `num_links * total_configurations` (n vertices in evaluate cost
times the iteration space).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Trivial single-line rewrite to match rustfmt. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-k-plex problem: given graph G=(V,E), vertex weights w, and integer
k>=1, find max-weight subset S subseteq V such that the induced
subgraph G[S] has maximum degree at most k-1 (i.e. every selected
vertex has at most k-1 selected neighbours). Generalizes
MaximumIndependentSet (the k=1 case) and is the complement-graph view
of maximum k-plex from the clique-relaxation literature.
- src/models/graph/maximum_co_k_plex.rs: MaximumCoKPlex<G,W,K>
parameterized by graph type, weight type, and K-multiplier. Only the
KN (runtime-k) variant registered initially per the issue's
"initially KN, K1/K2/... later" plan. Max<W::Sum> objective,
induced-degree feasibility, declare_variants! default + i32 variant,
canonical example via inventory (5-cycle weights (5,1,4,1,3) k=2,
optimum {0,2,4} value 12).
- src/unit_tests/models/graph/maximum_co_k_plex.rs: creation,
evaluate-feasible (issue optimum + smaller feasible), evaluate-
infeasible (degree-2 violation), brute-force solver, serialization.
- problemreductions-cli/src/commands/create/: schema-driven CLI maps
schema field bound_k to existing --k flag with semantic validation.
- docs/paper: problem-def block with C_5 worked example and k=1 ->
MaximumIndependentSet equivalence note; references.bib gains
Hernandez2016MolecularSimilarity and HosseinianButenko2022KDependent.
References: arXiv:1601.06693 (Hernandez et al., 2016) for the
molecular-similarity framing; doi:10.1016/j.dam.2021.10.015
(Hosseinian & Butenko, 2022) for the maximum k-dependent set view.
Closes #1015
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
MCES: given two directed edge-labelled graphs G1, G2, find a partial injective map f: U1 ⊆ V1 → V2 maximizing the number of preserved labelled arcs (u, λ, v) ∈ E1 with f(u), f(v) defined and (f(u), λ, f(v)) ∈ E2. Edge labels must match exactly; set semantics (no multiplicities); disconnected common subgraphs allowed; no secondary tie-break. - src/models/graph/maximum_common_edge_subgraph.rs: local LabelledArc + LabelledDigraph structs (does not extend the existing Graph trait hierarchy in this PR). dims = vec![|V2|+1; |V1|] with the +1 slot encoding ⊥. Max<i64> objective with injectivity feasibility on the matched slots. ProblemSchemaEntry + ProblemSizeFieldEntry for num_vertices_1/_2 and num_arcs_1/_2, declare_variants! default with complexity (num_vertices_2+1)^num_vertices_1. Canonical example via inventory from the issue's 5-vs-4-vertex instance with optimum value 5. - src/unit_tests/models/graph/maximum_common_edge_subgraph.rs: 12 tests covering creation, evaluate-feasible (optimum 5), evaluate-injectivity-violated, evaluate-fewer-preserved, brute-force solver, serialization. - problemreductions-cli/: new --graph-1 / --graph-2 flags with a LabelledDigraph parser; alias MCES. - docs/paper: problem-def block, display-name, MCES worked example. - docs/paper/references.bib: corrected per Crossref against the check-issue warning — Bahiense2012 first names (Laura/Gordana/Breno), Soule2021 author list (Soule/Reinharz/Sarrazin-Gendron/Denise/ Waldispuhl) and venue, Bokhari1981 volume (C-30). References: doi:10.1109/TC.1981.1675756 (Bokhari 1981), doi:10.1016/j.dam.2012.01.026 (Bahiense et al. 2012, polyhedral investigation), doi:10.1371/journal.pcbi.1008990 (Soule et al. 2021, RNA networks application). The direct `MaximumCommonEdgeSubgraph -> ILP` rule (#1019) is out of scope for this PR and will follow separately. Closes #1018 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Exact-cardinality edge-weighted clique: given a simple undirected
graph G=(V,E), edge weights w: E→R, and an integer k with 0≤k≤|V|,
find a vertex subset S with |S|=k forming a clique that maximizes
the sum of weights of edges induced by S. Edge weights may be
negative; k=0 and k=1 are admitted with objective value 0.
Distinct from the existing MaximumClique (vertex-weighted, no
exact-k) and KClique (decision problem with threshold |S|>=k).
- src/models/graph/maximum_edge_weighted_k_clique.rs:
MaximumEdgeWeightedKClique<W: WeightElement> with SimpleGraph fixed;
edge_weights vector aligned to graph.edges() order, runtime k field.
dims = vec![2; |V|]. Max<W::Sum> objective; infeasible when |S|≠k
or S is not a clique. declare_variants! default (SimpleGraph,i32)
plus (SimpleGraph,f64). Canonical example via inventory from the
issue's 4-vertex instance with negative weight (clique {0,1,2}
value 8 beats {0,1,3} value 6).
- src/unit_tests/models/graph/maximum_edge_weighted_k_clique.rs:
12 tests covering creation, evaluate-feasible (both optima),
evaluate-infeasible-wrong-size, evaluate-infeasible-not-clique,
brute-force solver, edge cases k=0 and k=1 (value 0), f64 variant,
serialization roundtrip, panic guards.
- docs/paper: problem-def block with worked example highlighting that
the optimum includes a negative edge; display-name entry; cites
Gouveia & Martins 2015 and Hunting/Faigle/Kern 2001.
- docs/paper/references.bib: Crossref-verified Gouveia2015MEWC
(author corrected to Pedro Martins, not Paulo as the issue body
said) and HuntingFaigleKern2001EWC.
References: doi:10.1007/s13675-014-0028-1 (Gouveia & Martins 2015,
sparse-graph compact formulations); doi:10.1016/S0377-2217(99)00449-X
(Hunting, Faigle & Kern 2001, Lagrangian relaxation).
The direct `MaximumEdgeWeightedKClique -> ILP` rule (#1021) is out
of scope for this PR and will follow separately.
Closes #1020
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Given a simple undirected graph G=(V,E), find a minimum-cardinality
edge set F ⊆ E such that every connected component of G - F is either
an isolated vertex or a highly connected graph on ≥3 vertices
(edge connectivity λ(H) > |V(H)|/2, strict). Components of size 2
are explicitly invalid. Weaker than clique-deletion: every K_k for
k≥3 is highly connected, but not every highly connected graph is a
clique.
- src/models/graph/highly_connected_deletion.rs: variables are EDGES
(x_e=1 means delete edge e). Min<i64> objective counts deletions;
infeasibility on any non-singleton component that is not highly
connected (and any 2-vertex component). Private edge_connectivity
helper computes λ via repeated max-flow with unit edge capacities
(fine for small components in tests). ProblemSchemaEntry,
ProblemSizeFieldEntry (num_vertices/num_edges), declare_variants!
default with complexity 2^num_edges. Canonical example via
inventory: K3 with leaf vertex 3 attached to 2 (4 vertices,
4 edges) — optimum deletes only (2,3), value 1.
- src/unit_tests/models/graph/highly_connected_deletion.rs: 17 tests
covering creation, evaluate-optimum, evaluate-zero-deletions-
infeasible, evaluate-delete-all-feasible, evaluate-infeasible
2-vertex-component and infeasible path-component, wrong-length
config guard, brute-force on canonical + a "double triangle"
discriminator instance (two K3's joined at a bridge — optimum 1)
to address the check-issue warning about example discriminatory
power, serialization, variant, plus edge_connectivity helper
tests (single vertex=0, single edge=1, P3=1, K3=2, K4=3).
- docs/paper: problem-def block with the K3-with-leaf worked example,
display-name entry; Crossref-verified BibTeX entries for Hüffner
et al. 2014 (TCBB) and Hartuv & Shamir 2000 (IPL), with proper
umlaut encoding H{"u}ffner per repo convention.
References: doi:10.1109/TCBB.2013.177 (Hüffner et al. 2014, partitioning
biological networks); doi:10.1016/S0020-0190(00)00142-3 (Hartuv &
Shamir 2000, HCS clustering algorithm).
The direct `HighlyConnectedDeletion -> ILP` rule (#1023) is out of
scope for this PR and will follow separately.
Closes #1022
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Classical directed-multigraph satisfaction problem: given D=(V,A) with
parallel arcs and loops allowed, decide whether a directed trail
exists that uses every arc exactly once. No start or end vertex is
fixed by the input. Empty-arc instance is accepted with the empty
trail; isolated vertices are ignored.
Polynomial-time solvable (O(num_vertices + num_arcs)) by the standard
Eulerian criterion plus Hierholzer construction, so this widens the
catalog beyond NP-hard problems.
- src/models/graph/eulerian_path.rs: EulerianPath { graph:
DirectedGraph }. dims = vec![m; m] where m = num_arcs (variable t
picks which arc occurrence is the t-th trail step); the brute-force
search space is m^m but the registry complexity reflects the
linear-time best-known algorithm. Or-typed feasibility: configuration
must be a permutation of {0..m-1} and consecutive arcs must chain
(end of arc t equals start of arc t+1). declare_variants! default
+ ProblemSchemaEntry + ProblemSizeFieldEntry. Canonical example via
inventory from the issue's 3-vertex 4-arc instance with parallel
arcs (yes-instance, witness config [0,2,3,1]).
- src/unit_tests/models/graph/eulerian_path.rs: 11 tests covering
creation, evaluate-valid-witness, evaluate-not-permutation,
evaluate-bad-trail, evaluate-out-of-range, evaluate-wrong-length,
brute-force yes (canonical) + brute-force no (the issue's 2-vertex
4-arc imbalanced counterexample), empty-arcs edge case (Or(true)
with the empty witness), serialization roundtrip, variant + name.
- problemreductions-cli/src/commands/create/schema_support.rs: wire
--graph (DirectedGraph) for EulerianPath via the existing parser.
- docs/paper: problem-def block with both the yes-instance and the
no-instance from the issue; display-name entry; references.bib gains
Crossref-verified BangJensenGutin2009Digraphs (J{\o}rgen Bang-Jensen,
o-slash) and Ebert1988ComputingEulerianTrails (J{"u}rgen Ebert,
u-umlaut) — corrected from the issue body's mojibake.
References: doi:10.1007/978-1-84800-998-1 (Bang-Jensen & Gutin 2009,
digraphs); doi:10.1016/0020-0190(88)90170-6 (Ebert 1988, computing
Eulerian trails).
The direct `EulerianPath -> ILP` rule (#1025) is out of scope for
this PR and will follow separately.
Closes #1024
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Biology-paper prize-collecting Steiner forest: given a network G with
nonnegative vertex prizes p(v), nonnegative edge costs c(e), and
tradeoff coefficients beta, omega, find a forest subgraph
F = (V_F, E_F) minimizing
beta * sum_{v notin V_F} p(v) + sum_{e in E_F} c(e) + omega * kappa(F)
where kappa(F) is the number of tree components (singleton selected
vertices count). Generalizes prize-collecting Steiner tree from one
connected tree to a forest; the artificial-root trick is deliberately
kept out of the base model and will live in the companion reduction
rule.
- src/models/graph/prize_collecting_steiner_forest.rs:
PrizeCollectingSteinerForest<G, W> with dims = vec![2; n+m] (vertex
bits then edge bits), Min<W::Sum> objective. Feasibility checks
edges-incident-to-selected-vertices and forest acyclicity; infeasible
→ Min(None). Canonical example via inventory from the issue's
3-vertex path with optimum [1,1,1, 1,0] value 5 (cost 1 + omega*2
components). declare_variants! default (SimpleGraph,i32) plus
(SimpleGraph,f64). Complexity 2^(num_vertices+num_edges).
- src/unit_tests/models/graph/prize_collecting_steiner_forest.rs:
13 tests — creation, evaluate-optimum, evaluate-full-path (value 9),
evaluate-three-singletons (value 6), evaluate-empty-forest
(value 12), evaluate-edge-without-endpoint-infeasible,
evaluate-cycle-infeasible (triangle selected entirely),
brute-force solver, serialization, f64 variant, panic guards.
- problemreductions-cli/: new --vertex-prizes / --edge-costs / --beta /
--omega flags via schema-driven create; mapping and fixture updates.
- docs/paper: problem-def block with worked example breakdown
(omitted-prize / edge-cost / component terms summing to 5);
display-name; Crossref-verified BibTeX for both Tuncbag et al.
papers (JCB 2013 and RECOMB 2012).
References: doi:10.1089/cmb.2012.0092 (Tuncbag et al. 2013, JCB);
doi:10.1007/978-3-642-29627-7_31 (Tuncbag et al. 2012, RECOMB).
The direct `PrizeCollectingSteinerForest -> SteinerTree` rule (#1027)
is out of scope for this PR and will follow separately.
Closes #1026
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Lexicographic objective on a directed multigraph with source s, sink t,
arc capacities u_e, and arc costs c_e: first maximize the s-t flow
value |f|, then among maximum-value flows minimize total arc cost
sum_e c_e f_e. Captures the CellRouter (Lummertz da Rocha et al. 2018)
model directly.
Architectural carve-out: the repo's Problem trait is discrete, so the
implementation restricts to INTEGRAL flows with dims=[c_e+1; m]. This
mirrors the existing MinimumEdgeCostFlow precedent and stays sound
for any rational instance by scaling. Documented in the module doc.
Lex encoding: Min<i64> with combined scalar
score = M * (max_possible_flow - flow_value) + cost
where M = sum_e c_e * u_e + 1 dominates any feasible cost — so lower
scores always prefer higher flow value first, breaking ties by lower
cost. Infeasible (capacity / conservation violations) → Min(None).
- src/models/graph/minimum_cost_maximum_flow.rs:
MinimumCostMaximumFlow { graph: DirectedGraph, source, sink,
capacities: Vec<i64>, costs: Vec<i64> }. Inherent helpers
flow_value(config) and total_cost(config) for tests. Canonical
example via inventory: V={0,1,2,3}, arcs [(0,1),(0,2),(1,2),
(1,3),(2,3)], capacities [2,1,1,1,2], costs [1,0,0,1,2] — optimum
config [2,1,1,1,2] with value 3 and cost 7. ProblemSchemaEntry +
ProblemSizeFieldEntry (num_vertices, num_arcs). declare_variants!
default with complexity (num_vertices+num_arcs)^6 (a conservative
polynomial placeholder justified by the LP formulation).
- src/unit_tests/models/graph/minimum_cost_maximum_flow.rs: 9 tests
covering creation, evaluate-optimum, evaluate-suboptimal-feasible,
evaluate-capacity-exceeded (infeasible), evaluate-conservation-
violated (infeasible), brute-force solver returning value 3 cost 7,
serialization, and the lex-tiebreaker test on a 4-vertex bottleneck
instance where two distinct max-value flows (value 1) exist with
costs 1 and 5 — brute-force must pick the cheaper one. The
tiebreaker test directly addresses the check-issue warning that
the original example admits a unique max-flow.
- problemreductions-cli/: new --source / --sink (usize) flags wired
via schema-driven create; --graph (DirectedGraph), --capacities,
--costs reused from the MECF wiring.
- docs/paper: problem-def block explaining the lex objective and the
integral-flow restriction, worked example with value/cost
breakdown; display-name; Crossref-verified BibTeX for
Lummertz da Rocha et al. 2018 (doi:10.1038/s41467-018-03214-y);
MIT 6.854 min-cost-flow notes as a @misc entry with URL.
References: doi:10.1038/s41467-018-03214-y (CellRouter, Nature Comms
2018); MIT 6.854 scribe notes for the standard min-cost-flow
equivalence.
The direct `MinimumCostMaximumFlow -> MinimumCostCirculation` rule
(#1031) is out of scope for this PR and will follow separately.
Closes #1029
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Classical minimum-cost circulation on a directed multigraph with
finite arc capacities u_e ≥ 0 and signed arc costs a_e ∈ R: find
g: E→R_{≥0} satisfying capacity bounds (0 ≤ g_e ≤ u_e) and flow
conservation at every vertex, minimizing sum_e a_e g_e.
This is the exact companion target for `MinimumCostMaximumFlow`
(reduction #1031) — the standard MCMF → MCCirc reduction uses a
sufficiently negative return arc from sink to source, which is why
signed costs must be supported in the base model.
Architectural carve-out (same as MinimumCostMaximumFlow #1029 and
MinimumEdgeCostFlow): the discrete Problem trait restricts to
INTEGRAL circulation with dims=[c_e+1; m]; sound for any rational
instance by scaling. Documented in the module doc.
- src/models/graph/minimum_cost_circulation.rs:
MinimumCostCirculation { graph: DirectedGraph, capacities: Vec<i64>,
costs: Vec<i64> } — no source/sink, conservation at every vertex.
Min<i64> objective; capacity-or-conservation violations → Min(None).
ProblemSchemaEntry + ProblemSizeFieldEntry (num_vertices, num_arcs).
declare_variants! default with conservative polynomial placeholder
(num_vertices+num_arcs)^6. Canonical example via inventory — a
3-vertex two-cycle instance discriminating between four feasible
alternatives (zero, cycle-A-only -2, cycle-B-only -3, both at
capacity -5) so round-trip tests have real discriminatory power,
addressing the check-issue warning about the issue's 2-vertex
example being too small.
- src/unit_tests/models/graph/minimum_cost_circulation.rs: 11 tests
covering creation, evaluate-optimum (config [2,2,1,1] → Min(-5)),
evaluate-zero, evaluate-cycle-A-only (-2), evaluate-cycle-B-only
(-3), evaluate-half-cycle-A (-4), evaluate-infeasible (capacity
exceeded, conservation violated), brute-force solver, serialization,
negative-cost-only-cycle smoke.
- problemreductions-cli/: --graph (DirectedGraph), --capacities,
--costs reused from MECF/MCMF wiring; new schema mapping for MCCirc.
- docs/paper: problem-def block with the two-cycle worked example
spelled out (per-unit costs, capacity bottlenecks), display-name
entry; reuses the existing mit6854MinCostFlow @misc bib entry
added with MCMF — no new references.bib changes.
References: MIT 6.854 (S2021) min-cost flow algorithms notes (shared
with #1029).
The direct `MinimumCostMaximumFlow -> MinimumCostCirculation` rule
(#1031) is out of scope for this PR and will follow separately.
Closes #1030
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Consensus-string problem under Hamming distance: given an alphabet
Σ = {0,...,q-1} and n equal-length strings s_1,...,s_n ∈ Σ^m, find a
center string c ∈ Σ^m minimizing max_i d_H(c, s_i). NP-hard (Frances
& Litman 1997, Lanctot et al. 1999), with extensive FPT and PTAS
literature (Gramm & Niedermeier 2003, Ma & Sun 2009, Li/Ma/Wang 2002).
Distinct from ClosestSubstring — every input string has the same
length as the center, so there is no window-selection decision.
- src/models/misc/closest_string.rs: ClosestString { alphabet_size,
strings: Vec<Vec<usize>> }. Validating constructor panics on
length mismatch or out-of-alphabet symbol. dims = vec![q; m].
Min<i64> objective (always feasible — every config in the cube is
a syntactically valid center). Inherent getters alphabet_size,
num_strings, string_length, total_length. ProblemSchemaEntry +
ProblemSizeFieldEntry with all four size fields. declare_variants!
default with complexity alphabet_size^string_length. Canonical
example via inventory from the issue's 4-string binary length-3
instance (optimal center [0,0,0], radius 2).
- src/unit_tests/models/misc/closest_string.rs: 11 tests covering
creation, evaluate at three different centers (c=000 → 2, c=100
→ 3, c=111 → 3), brute-force solver returning radius 2 over 8
candidates, three panic guards (empty input, length mismatch,
out-of-alphabet symbol), a q=3 length-2 ternary smoke test
(radius 2 over 9 candidates), and serialization.
- problemreductions-cli/: schema-driven create wires --alphabet-size
(usize) and --strings (Vec<Vec<usize>>) — reuses the existing
Vec<Vec<usize>> parser added with #994.
- docs/paper: problem-def block with all four Hamming distances
spelled out for c=000; display-name entry; Crossref-verified
Li/Ma/Wang 2002 (JACM) bib entry.
Reference: doi:10.1145/506147.506150 (Li, Ma & Wang 2002, JACM).
The direct `ClosestString -> ILP` rule (#1034) is out of scope for
this PR and will follow separately.
Closes #1032
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Window-selection generalization of ClosestString (#1032): given an alphabet Σ, n strings (NOT necessarily equal length), and a substring length ℓ, find a center c ∈ Σ^ℓ and start positions p_i selecting length-ℓ windows s_i[p_i .. p_i+ℓ) minimizing max_i d_H(c, s_i[p_i .. p_i+ℓ)). Motif-discovery model — NP-hard, no PTAS in general (Li, Ma & Wang 2002 JACM; Marx 2008). ClosestString is the special case where every input string has length exactly ℓ (single window per string). - src/models/misc/closest_substring.rs: ClosestSubstring { alphabet_size, strings: Vec<Vec<usize>>, substring_length }. Validating constructor panics on empty input, substring_length > min |s_i|, or out-of-alphabet symbol. dims concatenates ℓ center slots (domain {0..q-1}) with n window-start slots (domain {0..W_i-1} where W_i = |s_i| - ℓ + 1). Min<i64> objective, always feasible since every config in the cube is syntactically valid. Inherent getters alphabet_size, num_strings, substring_length, total_length, total_num_windows, num_window_choice_product (with saturating multiplication). ProblemSchemaEntry + ProblemSizeFieldEntry. declare_variants! default with complexity alphabet_size^substring_length * num_window_choice_product. Canonical example via inventory from the issue's 3 binary strings with ℓ=3 — optimum center [0,1,0] with window picks (0,1,0), radius 1 over 216 candidate configs. - src/unit_tests/models/misc/closest_substring.rs: 11 tests covering creation, evaluate at optimum (radius 1), evaluate at center [0,0,0] with all-zero windows (radius 2), evaluate at center [1,1,1] (radius >=1), brute-force solver, ClosestString reduction validation (substring_length = string_length → matches the #1032 canonical's radius 2), three panic guards (empty input, length mismatch, out-of-alphabet symbol), and serialization roundtrip. - problemreductions-cli/: schema-driven create wires --alphabet-size + --strings (reused from #1032) plus the new --substring-length (usize) flag. - docs/paper: problem-def block with the worked example listing all three window picks and per-window Hamming distances; display-name entry. Reuses the existing Li/Ma/Wang 2002 JACM BibTeX entry added with #1032 — no references.bib changes. Reference: doi:10.1145/506147.506150 (Li, Ma & Wang 2002, JACM) shared with #1032. The direct `ClosestSubstring -> ILP` rule (#1035) is out of scope for this PR and will follow separately. Closes #1033 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Classical protein-structure contact-map alignment: given two ordered
contact graphs G_1=(V_1,E_1) and G_2=(V_2,E_2), find an order-preserving
partial injective map f: V_1 → V_2 ∪ {unmatched} maximizing the number
of contacts {i,k} ∈ E_1 such that both i, k are matched and
{f(i), f(k)} ∈ E_2. Aliases: CMO, MaxCMO.
NP-hard with substantial literature on exact algorithms and integer
programming (Andonov, Malod-Dognin & Yanev 2011; Xie & Sahinidis 2007).
- src/models/graph/maximum_contact_map_overlap.rs:
MaximumContactMapOverlap { num_vertices_1, contacts_1, num_vertices_2,
contacts_2 }. Validating constructor normalizes each pair to sorted
form (u<v), rejects self-loops, duplicates, and out-of-range
endpoints. dims = vec![num_vertices_2 + 1; num_vertices_1] (value 0
encodes unmatched; value j+1 maps to vertex j of G_2). Max<i64>
objective; non-injective or non-order-preserving matched values →
Max(None). ProblemSchemaEntry + ProblemSizeFieldEntry; inherent
getters num_vertices_1/_2 and num_contacts_1/_2. declare_variants!
default with complexity (num_vertices_2+1)^num_vertices_1.
Canonical example via inventory: G_1 with 4 vertices and contacts
{(0,2),(1,3)}, G_2 with 5 vertices and contacts {(0,2),(0,3),(1,4)}
— optimum [1,2,4,5] preserves both contacts → Max(Some(2)).
- src/unit_tests/models/graph/maximum_contact_map_overlap.rs: 17 tests
covering creation, evaluate at optimum, all-unmatched, single-match,
non-injective Max(None), non-order-preserving Max(None), suboptimal
feasible (config [1,2,3,4] preserves 1 of 2 contacts), brute-force
solver returning Max(2), wrong-length and out-of-range guards,
serialization, alias resolution for CMO/MaxCMO, and three panic
guards (self-loop, duplicate contact, endpoint out of range).
- problemreductions-cli/: schema-driven create wires --num-vertices-1
/ --num-vertices-2 / --contacts-1 / --contacts-2 (Vec<(usize,usize)>
parser) via the existing CreateArgs + flag_map + tests fixture.
- docs/paper: problem-def block with the alignment table and the two
preserved-contact bullets; display-name; Crossref-verified BibTeX
for both Andonov-Malod-Dognin-Yanev 2011 and Xie-Sahinidis 2007
JCB papers (with N{\"o}el encoded per repo umlaut convention).
References: doi:10.1089/cmb.2009.0196 (Andonov, Malod-Dognin & Yanev
2011, JCB); doi:10.1089/cmb.2007.R007 (Xie & Sahinidis 2007, JCB).
The direct `MaximumContactMapOverlap -> ILP` rule (#1044) is out of
scope for this PR and will follow separately.
Closes #1043
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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## main #1067 +/- ##
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+ Coverage 97.93% 97.99% +0.06%
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Files 966 1046 +80
Lines 100154 107662 +7508
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+ Hits 98082 105507 +7425
- Misses 2072 2155 +83 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Net -83 lines across the four skill files touched by 694ef0c. auto-pipeline (-79): - Collapse Step 0a + 0b into a single picker block; the only difference was "filter by number" vs "sort and pick top" — now branched by whether the ISSUE env var is set. - Extract the boilerplate that was duplicated across all five subagent prompts (output-only-JSON-block contract, universal don'ts, malformed-JSON retry policy, severity vocabulary) into a single "Subagent Contract" section near the top. Each prompt now states only its scope and JSON shape. Drop the trailing "Reporting Contract" section (merged into the new one). - Trim the Board states table from 8 GraphQL IDs to the 3 columns the orchestrator actually writes (ready, on-hold, plus the Backlog it reads in Step 0). The IDs for In Progress / Review pool / Under review / Final review live in run-pipeline / review-pipeline where they are used. - Drop three rows from Common Mistakes that just echoed the spec (codex retry cap, increment SUBSTANTIVE_RETRIES, re-check after auto-fix); keep only the non-obvious cross-cutting traps. check-issue (-9 net): - Rule Check 5a no longer re-lists the literature fallback chain; one-line reference to Check 3c suffices. - Rule Check 5c verdict table drops the (severity: ...) annotations on Fail rows — severity classification is owned by auto-pipeline's Subagent Contract, not by check-issue itself. - Rule Check 5c drops the "cited reference does not contain the reduction" row that explicitly admitted overlap with Check 3c; add a one-line note that 3c handles that case. review-structural (-19): - Step 4b-4 (pred --via spot-check) was hedged out of its own purpose with a "fall back to 4b-2" escape hatch. Rewrite as a short focused step that uses pred --via when wired and is skipped (with a note) otherwise — no padding. review-quality (~2 net): - Replace the 4-criterion expansion of the round-trip rule (which was copy-pasted from review-structural Step 4b-3) with a single pointer to that source-of-truth section. Per the existing feedback_skill_no_duplication memory. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- MinimumDiscretePlanarInverseKinematics: simplify complexity string from "num_links * total_configurations" to "total_configurations" so it matches issue #994's stated O(prod_{j=1}^n m_j) baseline literally (the extra num_links factor was per-config feasibility-check work, not configs). - MinimumCostCirculation: add test_minimum_cost_circulation_issue_example_1030 that constructs issue #1030's verbatim 2-vertex example (arcs 0->1 cap=2 cost=3 and 1->0 cap=1 cost=-5, optimum -2). The existing richer 3-vertex canonical instance is kept as the primary discriminator.
Adds Sahni's multiplier-flow gadget: each Partition element becomes an item vertex whose multiplier amplifies a binary source choice into either 0 or a_i units entering a relay. A single bottleneck arc of capacity S/2 converts the target's "net inflow at least R" condition into the exact equality needed by Partition. Odd-S inputs reduce to a fixed infeasible 3-vertex instance. - src/rules/partition_integralflowwithmultipliers.rs: reduction impl with odd-S/even-S branches and witness extraction from source arcs - src/unit_tests/rules/partition_integralflowwithmultipliers.rs: 5 tests (closed-loop, structure on even-total YES, even-total NO exercises bottleneck, odd-total fixed NO, witness extraction) - src/rules/mod.rs: register module and example specs - docs/paper/reductions.typ: full theorem with construction, correctness proof, and worked example using the canonical fixture Closes #363. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Plaisted (1976) reduction from Decision Minimum Vertex Cover to
Comparative Containment (Garey & Johnson SP10). Given a unit-weight VC
instance (G = (V, E), K), the universe X = V is encoded as:
- For each vertex v, a reward set R_v = V \\ {v} with weight 1, so the
total R-weight equals n - |Y|.
- For each edge e = {u, v}, a penalty set S_e = V \\ {u, v} with weight
n + 1 that dominates the maximum possible reward whenever the edge is
uncovered.
- One budget set S_0 = V with weight n - K, which encodes the bound
through the resulting inequality K - |Y| >= (n + 1) * (#uncovered).
Source assertions mirror decisionminimumvertexcover_hamiltoniancircuit.rs:
unit weights are required, a negative bound emits a fixed unsatisfiable
target, and K >= n is handled as a trivial-YES instance (empty universe,
no R/S sets). Adds a ProblemSizeFieldEntry for ComparativeContainment
since it had no declared size_fields, and a paper theorem entry. Eight
unit tests cover structure counts, closed-loop YES/NO, witness
extraction, both trivial-YES branches, the trivial-NO branch, and the
weight-assertion guard.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Implements Salloum et al. 2025 quantum-annealing IK encoding: one-hot binary lifting of sampled orientations, quadratic position-error term, one-hot exactly-one penalty, and pair-feasibility penalty for forbidden adjacent (a,b) pairs. Penalty constants are chosen above the maximum possible position-error savings, so every QUBO minimizer decodes to a feasible source configuration. Also adds num_orientation_samples() getter on the source model. Closes #995. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
…nTime reduction (#472) Implements Lawler 1978 precedence-constrained 1||sum w_j C_j encoding: each vertex becomes a unit-length task with weight 1, each edge becomes a zero-length task with weight 1, and the precedence constraints enforce the OLA ordering. The minimum weighted completion time recovers the OLA objective plus an additive shift d_max*n*(n+1)/2. Also relaxes the target model's validator to allow zero-length edge jobs needed by Lawler's construction. Closes #472. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
) Direct partition-matroid embedding: a 3DM matching is precisely the common independent set of three partition matroids, one over each coordinate axis (X, Y, Z). The reduction copies the triple set, builds the three partition matroids by grouping triples that share each coordinate, and uses identity solution extraction. Closes #857. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
…on (#848) Identity instance mapping: a clique cover {C_1, ..., C_k} of G is a valid intersection-graph basis with the same cardinality k, since each vertex's labels are exactly the cliques it belongs to and each edge is witnessed by a shared label. The witness extraction labels each edge by a shared intersection slot. Closes #848. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
…reduction (#827) Standard G&J transformation: a Numerical 3DM instance with triples (X, Y, Z, bound) maps to an NMTS instance where the X and Y triples form the pair sizes and each Z element becomes a target sum equal to bound + z. The witness is recovered by multiset matching against the source's third coordinate. Source u64 sizes are checked-cast to i64 with a documented overflow guard. Closes #827. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Direct boolean ILP encoding for the Co-k-Plex problem: each vertex gets
a binary variable, the objective maximizes the weighted sum, and a
per-vertex constraint bounds the induced degree by k - 1 using the
linear inequality sum_{u in N(v)} y_u <= (k - 1) + M * (1 - y_v).
Closes #1016.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Boolean ILP encoding finds a one-to-one vertex correspondence between G1 and G2 that maximizes the number of preserved edges. Variables x[i,k] encode whether v_i in G1 maps to v_k in G2, with row/column sum-at-most-1 constraints enforcing a partial injection, and edge-preservation variables y[(i,j),(k,l)] linearized via the standard product-with-binaries pattern. Closes #1019. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Six unit tests covering: - ILP structure: variable + constraint counts and Maximize sense - closed-loop on the canonical issue instance (objective = 2) - trivial no-contacts instance - brute-force vs ILP optimum - order-preservation rejection of crossing alignments - extract_solution encoding (j -> j+1, unmatched -> 0) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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Self-contained gadget construction that maps a `KColoring(G, q)` instance to a `BicliqueCover` instance on `4n` vertices with rank `n + q`. Each source vertex `v` contributes diagonal/guard/compat edges; guard-anchor edges force `n` bicliques to be spent on guards, leaving at most `q` bicliques to encode color classes under the sub-biclique semantics. Wires up `ReduceTo<BicliqueCover>` for `KColoring<KN, SimpleGraph>`, registers the module in `src/rules/mod.rs`, and adds a canonical example spec (P_2 with q=2) for the example-db. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
…1058) Nine tests covering: - Closed-loop brute force on the trivial n=1, q=1 instance (8 binary vars). - Structural checks of `num_vertices = 4n`, `rank = n + q`, and the exact edge formula `2 n (n-1) - 4 m + 3 n` on a path and on K_4. - Explicit edge enumeration for n=2 P_2. - NO-instance check: K_4 with q=3 has no proper coloring. - Forward-witness construction on P_3, C_4, K_3: verifies the guard + color biclique cover is valid and that `extract_solution` recovers a proper q-coloring. - Adjacent-grouping rejection: confirms a witness merging adjacent source vertices into one color biclique fails the sub-biclique check. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds the paper entry next to the existing KColoring -> Clustering rule, summarizing the gadget construction (diagonal, compatibility, guard-anchor, guard-compat edges), the bidirectional correctness argument, the edge count formula `2 n (n-1) - 4 m + 3 n`, and the diagonal-biclique solution extraction. References Karp 1972, Garey-Johnson 1979, Orlin 1977 as background. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The forward_witness helper in src/rules/kcoloring_bicliquecover.rs was gated with cfg(any(test, feature = "example-db")), but its only caller (canonical_rule_example_specs) is gated with cfg(feature = "example-db"). Under cfg(test) without the example-db feature, the function was compiled but unused, tripping clippy's dead-code lint. Tighten the gate to cfg(feature = "example-db") to match its caller. The test file maintains its own build_forward_witness copy, so test coverage is unaffected. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Implement the Chandran-Issac-Karrenbauer (IPEC 2016, Theorem 6) polynomial
reduction from 3-SAT to BicliqueCover with logarithmic rank. The reduction
normalizes the source formula (split each x_i into t_i, f_i with exactly-one
clauses; pad to n = 2^ell normalized variables) and then assembles a bipartite
gadget with crown H_n, clause matchings P_i, domino gadgets S_j, guard Q, and
forcing matching Y. Solution extraction identifies the biclique B_1 covering
s_{1,1}^u s_{1,1}^v (skipping Y-touching free-edge bicliques) and reads off
the normalized assignment via h_i^u in B_1, then maps back to source variables.
Includes a canonical rule example with a hand-built forward witness on the
smallest case (1 source variable, 1 source clause, rank 18). Free-edge
biclique enumeration follows Lemma 16 (H-S, P-P, P-Q, H-P, S_1-P sets).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds seven unit tests covering the new reduction: - Structural sizes for the smallest source (n=2, ell=1, m=3, rank=18). - Structural sizes for the four-variable example from the issue body (n=8, ell=3, m=10, rank=26) — the post-normalization rank is higher than the issue's 22 because we faithfully emit the exactly-one clauses. - Construction termination on a UNSAT formula. - extract_solution reading B_1 (positive-literal h-vertex) from a hand-built partial witness. - extract_solution skipping Y-touching free-edge bicliques. - Two-variable instance constructs without index-out-of-bounds. - Full closed-loop on the smallest source using the canonical forward witness from the example-db builder (gated on the example-db feature). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Insert reduction-rule("KSatisfiability", "BicliqueCover", ...) block into
docs/paper/reductions.typ with normalization, construction, correctness,
and solution-extraction sections.
- Add chandran_et_al:LIPIcs.IPEC.2016.11 BibTeX entry to references.bib.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Gate `use crate::traits::Problem;` behind `cfg(feature = "example-db")` in the unit test, matching the feature gate on the only test that uses `evaluate` (clippy `--features ilp-highs` flagged it as unused). - Fix Typst syntax error in the reduction-rule proof: stray `$` inside `$x_i = ($h_i^u in B_1)$` produced an unclosed delimiter; corrected to `$x_i = (h_i^u in B_1)$` so `make paper` compiles. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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Implements ShortestCommonSuperstring (Garey & Johnson SR9, P157) as a minimization problem: given an alphabet Sigma and a set R of strings, find a shortest w in Sigma^* that contains every r in R as a contiguous substring. Uses Min<usize> value, fixed-length configuration with a sentinel padding symbol (mirroring sibling ShortestCommonSupersequence), and max_length derived in new() as the sum of input string lengths. Includes problem-def paper entry with CeTZ embedding diagram, canonical example_db instance, and unit tests covering the three issue examples (optima 9, 8, 7; the small one verified by brute force). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Implements the bounded integer variant of Quadratic Programming (G&J MP2):
minimize sum_i (c_i*y_i^2 + d_i*y_i) over y in {-K, ..., K}^m subject to
linear inequality constraints x . y <= b. NP-hardness follows from Sahni
(1974) PARTITION -> QP, whose construction lands on {0,1}^m.
- Value = Min<f64>; infeasible configs return Min(None)
- dims = vec![2*bound + 1; num_vars] with y_i = config_i - bound
- Reuses LinearConstraint from the ILP model
- Canonical example reproduces the Sahni PARTITION encoding for a=(1,1,2):
optimum y=(1,1,0) with objective 0
- Paper entry + Vavasis (1990) NP-membership reference
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Witness reduction specialising Garey & Johnson SP19 to K = 2: copy element sizes verbatim into a SumOfSquaresPartition with two groups. Source YES iff the optimal target witness is a balanced split, which Partition::evaluate accepts via identity solution extraction. Singleton sources take a sentinel path (target on two unit elements) that returns the all-zero source configuration, correctly classified as NO. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Implements the canonical Garey & Johnson MS13 reduction: given a MaxCut
instance (G, w) on SimpleGraph with nonnegative i32 edge weights, take
the weighted adjacency matrix A as the MinimumMatrixCover instance. The
identity sum_{i,j} a_ij f(i) f(j) = 2W - 4 * cut(S) makes MaxCut and
MinimumMatrixCover equivalent up to the constant 2W, and the binary
encoding (config[i] = 1 iff i in S iff f(i) = +1) makes solution
extraction the identity map.
- Source variant: MaxCut/SimpleGraph/i32 with nonneg precondition; the
reduction panics on any negative edge weight, mirroring the model's
nonnegative-matrix invariant.
- Overhead: num_rows = num_vertices.
- Closed-loop tests cover C_4 (unit weights), weighted P_3, K_3, plus
the algebraic identity check on every sign assignment and a
negative-weight precondition test.
- Canonical example_db builder uses C_4 with unit weights.
- Paper entry under docs/paper/reductions.typ with construction,
correctness proof, precondition note, and a worked C_4 walk-through.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Berlekamp-McEliece-van Tilborg (1978) / G&J MS7 construction: each triple becomes a column of a 3q x m binary parity-check matrix with all-ones syndrome; minimum weight codeword = q iff a perfect 3DM matching exists. Witness reduction (Or -> Min<usize>) with a 1x1 sentinel for q=0 or empty triple set. Tests cap at q=3, 5 triples (2^5 target search space). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…tation rule (#434) Register the Decision<OptimalLinearArrangement> variant (prerequisite) and implement the reduction to ConsecutiveOnesMatrixAugmentation via the edge-vertex incidence matrix with augmentation bound k - |E|, including the m=0 always-YES sentinel and the k<m genuine-NO cyclic-overlap sentinel. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Surfaced under --features example-db; would fail clippy -D warnings. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…leanup (#434) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Fixes inline-comment spacing flagged by cargo fmt --check. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Typst deprecated the `sect` set-intersection operator in favor of `inter`; silences the only remaining make paper warning. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
… variant under MP2 name Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
isPANN
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The Planar3Satisfiability -> MinimumGeometricConnectedDominatingSet reduction emitted a geometrically disconnected target (point bands >=1.5 apart, copies/clauses spaced 3 apart, radius 1), so the unit-disk graph admits no connected dominating set and every satisfiable source mapped to an infeasible target. Confirmed unsound via /verify-reduction (6004 checks). Removed the rule, its test, mod.rs registration, example-spec wiring, and the paper reduction-rule entry. The MinimumGeometricConnectedDominatingSet model is retained as a sound orphan node pending a correct reduction (+ a GCD->ILP rule for closed-loop tests); #377 stays open. Cleanups: dedupe PCSF feasibility into a shared forest_components() helper (removing an unreachable branch); dedupe KColoring forward_witness between rule and test; trim the oversized SCSS brute-force test; minor KISS nits (eulerianpath y_idx wrapper, OLA is_valid_solution, CMO overhead). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
Serial batch-add of 13 Backlog
[Model]issues + 26 Backlog[Rule]issues on a single branch (batch-add-models), driven by the/auto-pipelineorchestrator. Each commit: full Rust impl + unit tests + schema-driven CLI + Typst entry + Crossref-verified BibTeX.Closes
Merging this PR closes the following 39 issues (13 models + 26 rules):
Closes #363, closes #385, closes #393, closes #413, closes #434, closes #472, closes #827, closes #846, closes #847, closes #848, closes #857, closes #913, closes #916, closes #925, closes #994, closes #995, closes #1015, closes #1016, closes #1018, closes #1019, closes #1020, closes #1021, closes #1022, closes #1023, closes #1024, closes #1025, closes #1026, closes #1027, closes #1029, closes #1030, closes #1031, closes #1032, closes #1033, closes #1034, closes #1035, closes #1043, closes #1044, closes #1057, closes #1058.
Models added (13)
MinimumDiscretePlanarInverseKinematicsMaximumCoKPlexMaximumCommonEdgeSubgraphMaximumEdgeWeightedKCliqueHighlyConnectedDeletionEulerianPathPrizeCollectingSteinerForestMinimumCostMaximumFlowM*(max-flow)+costencoding; integral-flow carve-out (mirrors MECF)MinimumCostCirculationClosestStringq^mbrute-forceClosestSubstring[q;ℓ]++[W_i]MaximumContactMapOverlapShortestCommonSuperstring[alphabet_size+1; max_length]with padding sentinel; G&J SR9; distinct from ShortestCommonSupersequenceRules added (26)
Partition → IntegralFlowWithMultipliersMinimumVertexCover → ComparativeContainmentOptimalLinearArrangement → SequencingToMinimizeWeightedCompletionTimeNumerical3DimensionalMatching → NumericalMatchingWithTargetSumsMinimumMaximalMatching → MaximumAchromaticNumberMinimumMaximalMatching → MinimumMatrixDominationMinimumCoveringByCliques → MinimumIntersectionGraphBasisThreeDimensionalMatching → ThreeMatroidIntersectionExactCoverBy3Sets → BoundedDiameterSpanningTreeMinimumDiscretePlanarInverseKinematics → QUBOPMaximumCoKPlex → ILPMaximumCommonEdgeSubgraph → ILPMaximumEdgeWeightedKClique → ILPHighlyConnectedDeletion → ILPis_feasible_cluster/induced_edge_countEulerianPath → ILPMinimumCostMaximumFlow → MinimumCostCirculation(t,s)with capacityU, cost-(1+Σc_e)ClosestString → ILPClosestSubstring → ILPR ≤ ℓupper bound for HiGHS perfMaximumContactMapOverlap → ILPKSatisfiability/K3 → BicliqueCoverKColoring → BicliqueCoverMaxCut → MinimumMatrixCoverDecision<OptimalLinearArrangement> → ConsecutiveOnesMatrixAugmentationPartition → SumOfSquaresPartitionPrizeCollectingSteinerForest → SteinerTreeThreeDimensionalMatching → MinimumWeightDecodingTest plan
cargo clippy --all-targets --features ilp-highs -- -D warningscleancargo test --features "ilp-highs example-db" --libgreen — 5376 passed, 0 failedmake paperbuilds the Typst PDF cleanly (completeness check passes; retainedMinimumGeometricConnectedDominatingSetorphan node is covered by its problem-def)pred create --example <Model> | pred solve --solver brute-forcefor each new modelpred path Source Target --json+ via-reduction solve for each new rule — all agree with source-direct brute force / ILP🤖 Generated with Claude Code