Loop Calculus and Bootstrap-Belief Propagation for Perfect Matchings on Arbitrary Graphs

TL;DR

This paper introduces a novel approach combining Loop Calculus and Bootstrap-Belief Propagation to compute the Partition Function and solve the Minimum Weight Perfect Matching problem on arbitrary graphs, improving accuracy and convergence.

Contribution

It extends Loop Calculus to the Perfect Matching problem and develops a Bootstrap-Belief Propagation method for approximate solutions and bounds on MWPM.

Findings

Bootstrap-and-Contract converges reliably.

Provides empirically tight upper bounds for MWPM.

Demonstrates effectiveness on various weighted PM problems.

Abstract

This manuscript discusses computation of the Partition Function (PF) and the Minimum Weight Perfect Matching (MWPM) on arbitrary, non-bipartite graphs. We present two novel problem formulations - one for computing the PF of a Perfect Matching (PM) and one for finding MWPMs - that build upon the inter-related Bethe Free Energy, Belief Propagation (BP), Loop Calculus (LC), Integer Linear Programming (ILP) and Linear Programming (LP) frameworks. First, we describe an extension of the LC framework to the PM problem. The resulting formulas, coined (fractional) Bootstrap-BP, express the PF of the original model via the BFE of an alternative PM problem. We then study the zero-temperature version of this Bootstrap-BP formula for approximately solving the MWPM problem. We do so by leveraging the Bootstrap-BP formula to construct a sequence of MWPM problems, where each new problem in the sequence is formed by contracting odd-sized cycles (or blossoms) from the previous problem. This Bootstrap-and-Contract procedure converges reliably and generates an empirically tight upper bound for the MWPM. We conclude by discussing the relationship between our iterative procedure and the famous Blossom Algorithm of Edmonds '65 and demonstrate the performance of the Bootstrap-and-Contract approach on a variety of weighted PM problems.