An approximation algorithm for counting contingency tables

TL;DR

This paper introduces a randomized approximation algorithm for counting contingency tables with certain smooth margins, achieving quasi-polynomial complexity and leveraging advanced sampling and matrix scaling techniques.

Contribution

It presents a novel approximation algorithm for counting contingency tables with smooth margins, extending the applicability of Monte Carlo and matrix scaling methods.

Findings

Algorithm has quasi-polynomial complexity for smooth margins.

Applicable to classes of margins with bounded ratios.

Uses advanced sampling and matrix scaling techniques.

Abstract

We present a randomized approximation algorithm for counting contingency tables, mxn non-negative integer matrices with given row sums R=(r_1, ..., r_m) and column sums C=(c_1, ..., c_n). We define smooth margins (R,C) in terms of the typical table and prove that for such margins the algorithm has quasi-polynomial N^{O(ln N)} complexity, where N=r_1+...+r_m=c_1+...+c_n. Various classes of margins are smooth, e.g., when m=O(n), n=O(m) and the ratios between the largest and the smallest row sums as well as between the largest and the smallest column sums are strictly smaller than the golden ratio (1+sqrt{5})/2 = 1.618. The algorithm builds on Monte Carlo integration and sampling algorithms for log-concave densities, the matrix scaling algorithm, the permanent approximation algorithm, and an integral representation for the number of contingency tables.