Enumerating contingency tables via random permanents

TL;DR

This paper introduces a polynomial-time randomized algorithm to approximate the total weight of contingency tables with specified margins and weights, leveraging permanents of random matrices and log-concave functions.

Contribution

It presents a novel randomized algorithm that efficiently approximates the weighted count of contingency tables, connecting permanents of random matrices with combinatorial enumeration.

Findings

Algorithm provides bounds for total weight T with high accuracy.

Approximation T' is efficiently computable and asymptotically accurate.

Applications include counting integer flows in graphs.

Abstract

Given m positive integers R=(r_i), n positive integers C=(c_j) such that sum r_i = sum c_j =N, and mn non-negative weights W=(w_{ij}), we consider the total weight T=T(R, C; W) of non-negative integer matrices (contingency tables) D=(d_{ij}) with the row sums r_i, column sums c_j, and the weight of D equal to prod w_{ij}^{d_{ij}}. We present a randomized algorithm of a polynomial in N complexity which computes a number T'=T'(R,C; W) such that T' < T < alpha(R, C) T' where alpha(R,C) = min{prod r_i! r_i^{-r_i}, prod c_j! c_j^{-c_j}} N^N/N!. In many cases, ln T' provides an asymptotically accurate estimate of ln T. The idea of the algorithm is to express T as the expectation of the permanent of an N x N random matrix with exponentially distributed entries and approximate the expectation by the integral T' of an efficiently computable log-concave function on R^{mn}. Applications to counting integer flows in graphs are also discussed.