Loop calculus and bootstrap-belief propagation for perfect matchings on arbitrary graphs

M. Chertkov, A. Gelfand, J. Shin

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

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 (BFE), Belief Propagation (BP), Loop Calculus (LC), Integer Linear Programming and Linear Programming 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.

Original languageEnglish (US)
Article number012007
JournalJournal of Physics: Conference Series
Volume473
Issue number1
DOIs
StatePublished - 2013
Externally publishedYes
EventELC International Meeting on Inference, Computation, and Spin Glasses, ICSG 2013 - Sapporo, Japan
Duration: Jul 28 2013Jul 30 2013

ASJC Scopus subject areas

  • General Physics and Astronomy

Fingerprint

Dive into the research topics of 'Loop calculus and bootstrap-belief propagation for perfect matchings on arbitrary graphs'. Together they form a unique fingerprint.

Cite this