Parity Separation: A Scientifically Proven Method for Permanent Weight Loss

TL;DR

The paper introduces parity separation, a new method to reduce weighted perfect matching counting problems to unweighted instances, enabling novel complexity results and applications in computational graph theory.

Contribution

It presents parity separation, a novel weight removal technique for counting perfect matchings, with new complexity classifications and applications in graph matching problems.

Findings

Provides an alternative #P-completeness proof for unweighted perfect matchings.

Establishes C=P-completeness for graph perfect matching count equality.

Offers a tight lower bound under #ETH for counting unweighted perfect matchings.

Abstract

Given an edge-weighted graph G, let PerfMatch(G) denote the weighted sum over all perfect matchings M in G, weighting each matching M by the product of weights of edges in M. If G is unweighted, this plainly counts the perfect matchings of G. In this paper, we introduce parity separation, a new method for reducing PerfMatch to unweighted instances: For graphs G with edge-weights -1 and 1, we construct two unweighted graphs G1 and G2 such that PerfMatch(G) = PerfMatch(G1) - PerfMatch(G2). This yields a novel weight removal technique for counting perfect matchings, in addition to those known from classical #P-hardness proofs. We derive the following applications: 1. An alternative #P-completeness proof for counting unweighted perfect matchings. 2. C=P-completeness for deciding whether two given unweighted graphs have the same number of perfect matchings. To the best of our knowledge, this is the first C=P-completeness result for the "equality-testing version" of any natural counting problem that is not already #P-hard under parsimonious reductions. 3. An alternative tight lower bound for counting unweighted perfect matchings under the counting exponential-time hypothesis #ETH. Our technique is based upon matchgates and the Holant framework. To make our #P-hardness proof self-contained, we also apply matchgates for an alternative #P-hardness proof of PerfMatch on graphs with edge-weights -1 and 1.