A fast parallel algorithm for minimum-cost small integral flows

TL;DR

This paper introduces a novel randomized parallel algorithm for solving small integral minimum-cost flow problems efficiently, leveraging polynomial tests over finite fields to achieve significant speedups for flows of logarithmic size.

Contribution

It develops a new approach reducing the minimum-cost flow problem to polynomial tests, enabling fast parallel algorithms for small flow values.

Findings

Algorithm runs in O(k log (kn) + log^2 (kn)) time on PRAM

Achieves an RNC^2 algorithm for flows of size O(log n)

Uses 2^{k}(kn)^{O(1)} processors with exponentially small error probability

Abstract

We present a new approach to the minimum-cost integral flow problem for small values of the flow. It reduces the problem to the tests of simple multi-variate polynomials over a finite field of characteristic two for non-identity with zero. In effect, we show that a minimum-cost flow of value k in a network with n vertices, a sink and a source, integral edge capacities and positive integral edge costs polynomially bounded in n can be found by a randomized PRAM, with errors of exponentially small probability in n, running in O(k\log (kn)+\log^2 (kn)) time and using 2^{k}(kn)^{O(1)} processors. Thus, in particular, for the minimum-cost flow of value O(\log n), we obtain an RNC^2 algorithm.