problemreductions 0.1.0

A library for reducing NP-hard problems
Documentation

100-Problem-Reductions

CI codecov Docs PDF Manual License: MIT

A Rust library for NP-hard problem definitions and reductions. We aim to implement >100 NP-hard problems and reductions rule between them, under the assistance of AI.

Installation

Add to your Cargo.toml (not yet available):

[dependencies]
problemreductions = "0.1"

Quick Start

use problemreductions::prelude::*;
use problemreductions::models::optimization::ILP;

// Create an Independent Set problem on a path graph
let problem = IndependentSet::<i32>::new(4, vec![(0, 1), (1, 2), (2, 3)]);

// Reduce to Integer Linear Programming
let reduction = ReduceTo::<ILP>::reduce_to(&problem);
let ilp = reduction.target_problem();

// Solve with ILP solver (efficient for larger instances)
let solver = ILPSolver::new();
let ilp_solution = solver.solve(ilp).unwrap();

// Extract solution back to original problem
let solution = reduction.extract_solution(&ilp_solution);
assert_eq!(solution.iter().sum::<usize>(), 2); // Max IS size is 2

Contributing

Authorship Recognition

Contribute 10 non-trivial reduction rules and you will be automatically added to the author list of the paper. To facilitate the development, we provide the AI tools to help developers implement their plans. Developers still need to carefully design the test cases and verify the correctness of the reduction rules.

Step by step

  1. Find or create a GitHub issue describing your proposal, which rule to add and how to test it. You may have some discussions with other developers to refine the proposal.
  2. Write a detailed plan in docs/plans/issue-<number>-<slug>.md.
    • If you use Claude Code, it is recommended to use the issue-to-pr skill. The skill will invoke superpowers:brainstorm to help you write a detailed plan: 
      /issue-to-pr <issue-number>
      Afterwards, the skill creates a PR starting with [action], which automatically triggers Claude CI to implement the plan.
    • Otherwise, you can write a detailed plan manually (sample). Then create a PR manually. You can start implementing this plan on the CI machine by adding [action] at the start of the PR description or comments.

Developer Commands

Run make help to see all available targets. See CLAUDE.md for the full command list and architecture details.

Acknowledgments

This project draws inspiration from the following packages:

  • ProblemReductions.jl — Julia library for computational problem reductions. Our problem trait hierarchy, reduction interface (ReduceTo/ReductionResult), and graph-based reduction registry are directly inspired by this package.
  • UnitDiskMapping.jl — Julia package for mapping problems to unit disk graphs. Our unit disk graph (King's subgraph / triangular lattice) reductions and the copy-line method are based on this implementation.
  • qubogen — Python library for generating QUBO matrices from combinatorial problems. Our QUBO reduction formulas (Vertex Cover, Graph Coloring, Set Packing, Max-2-SAT, binary ILP) reference the implementations in this package.

Related Projects

  • Karp — A DSL (built on Racket) for writing and testing Karp reductions between NP-complete problems (PLDI 2022 paper). Focused on education and proof verification rather than a solver pipeline.
  • Complexity Zoo — Comprehensive catalog of 550+ computational complexity classes (Scott Aaronson).
  • A Compendium of NP Optimization Problems — Online catalog of NP optimization problems with approximability results (Crescenzi & Kann).
  • Computers and Intractability (Garey & Johnson, 1979) — The classic reference cataloging 300+ NP-complete problems with reductions. The most cited book in computer science.

License

MIT License - see LICENSE for details.