kwok 1.1.0

A fast maximum weight bipartite matching algorithm from 'A Faster Algorithm for Maximum Weight Matching on Unrestricted Bipartite Graphs'

Kwok

A Cython-based implementation of "A Faster Algorithm for Maximum Weight Matching on Unrestricted Bipartite Graphs" that supports multiple Python versions and platforms.

About

Kwok implements a fast maximum weight bipartite matching algorithm based on the paper "A Faster Algorithm for Maximum Weight Matching on Unrestricted Bipartite Graphs" (https://arxiv.org/abs/2502.20889). The algorithm achieves runtime O(E^1.4 + LR) estimated from experimental tests on random graphs where |L| <= |R|.

The algorithm is particularly efficient for dense graphs and outperforms traditional implementations of the Hungarian algorithm in many practical scenarios.

Installation

pip install kwok

Usage

import kwok

# Example usage
L_size = 3  # Number of vertices in left partition
R_size = 3  # Number of vertices in right partition
adj = [
    [(0, 10), (1, 5), (2, 1)],  # Edges from left vertex 0
    [(0, 2), (1, 15), (2, 0)],  # Edges from left vertex 1
    [(0, 0), (1, 8), (2, 12)]   # Edges from left vertex 2
]

# Solve the matching problem
matching = kwok.kwok(L_size, R_size, adj)
print("Left pairs:", matching.left_pairs)
print("Right pairs:", matching.right_pairs)
print("Total weight:", matching.total_weight)

API Reference

kwok(L_size, R_size, adj)

Computes the maximum weight matching in a bipartite graph.

Parameters:

• L_size (int): Number of vertices in left partition (L)
• R_size (int): Number of vertices in right partition (R)
• adj (list): Adjacency list where each element is a list of (vertex, weight) tuples representing edges from a vertex in L to vertices in R

Returns:

• Matching: A dataclass with the following attributes:
  • left_pairs (list): Maps L vertices to their matched R vertices (-1 if unmatched)
  • right_pairs (list): Maps R vertices to their matched L vertices (-1 if unmatched)
  • total_weight (int|float): Total weight of the matching

Note: Integer weights are not required, but using integers may improve performance.

Development

To build the package locally:

pip install -e .

Building wheels with cibuildwheel

To build wheels for multiple Python versions and platforms, we use cibuildwheel.

cibuildwheel is a powerful tool that:

• Builds wheels for CPython and PyPy on Windows, macOS, and Linux
• Handles platform-specific wheels (including manylinux)
• Tests the built wheels in isolated environments
• Integrates with CI services like GitHub Actions

Local testing with cibuildwheel

To test cibuildwheel locally:

pip install cibuildwheel
python -m cibuildwheel --platform linux

Available platforms are: linux, windows, and macos.

CI integration

This project is configured to automatically build wheels using GitHub Actions. See the .github/workflows/build_wheels.yml file for the configuration.