3.8 Proceedings Paper

Fractionally Log-Concave and Sector-Stable Polynomials: Counting Planar Matchings and More

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3406325.3451123

Keywords

Counting and Sampling; Random Walk; Geometry of Polynomials; Spectral Independence

Funding

  1. NSF [CCF-2045354]

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This study presents a fully polynomial time randomized approximation scheme for counting matchings in planar graphs and discusses methods for sampling and counting monomer-dimer systems. The research shows that multi-site Glauber dynamics can efficiently mix on specific graph families, providing a new tool for establishing spectral independence based on polynomial geometry. This work also demonstrates the concept of fractional log-concavity in avoiding roots of polynomials in a complex plane sector.
We show fully polynomial time randomized approximation schemes (FPRAS) for counting matchings of a given size, or more generally sampling/counting monomer-dimer systems in planar, notnecessarily-bipartite, graphs. While perfect matchings on planar graphs can be counted exactly in polynomial time, counting non-perfect matchings was shown by Jerrum (J Stat Phys 1987) to be #P-hard, who also raised the question of whether efficient approximate counting is possible. We answer this affirmatively by showing that the multi-site Glauber dynamics on the set of monomers in a monomer-dimer system always mixes rapidly, and that this dynamics can be implemented efficiently on downward-closed families of graphs where counting perfect matchings is tractable. As further applications of our results, we show how to sample efficiently using multi-site Glauber dynamics from partition-constrained strongly Rayleigh distributions, and nonsymmetric determinantal point processes. In order to analyze mixing properties of the multi-site Glauber dynamics, we establish two notions for generating polynomials of discrete set-valued distributions: sector-stability and fractional log-concavity. These notions generalize well-studied properties like real-stability and log-concavity, but unlike them robustly degrade under useful transformations applied to the distribution. We relate these notions to pairwise correlations in the underlying distribution and the notion of spectral independence introduced by Anari et al. (FOCS 2020), providing a new tool for establishing spectral independence based on geometry of polynomials. As a byproduct of our techniques, we show that polynomials avoiding roots in a sector of the complex plane must satisfy what we call fractional log-concavity; this generalizes a classic result established by Garding (J Math Mech 1959) who showed homogeneous polynomials that have no roots in a half-plane must be log-concave over the positive orthant.

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