Fast Approximation Algorithms for Multicommodity Flow Problems

All previously known algorithms for solving the multicommodity flow problem with capacities are based on linear programming. The best of these algorithms uses a fast matrix multiplication algorithm and takes O(k3.5n3m0.5 log(nDU)) time for the multicommodity flow problem with integer demands and at least O(k2.5n2m0.5 log(nϵ−1DU)) time to find an approximate solution, where k is the number of commodities, n and m denote the number of nodes and edges in the network, D is the largest demand, and U is the largest edge capacity. As a consequence, even multicommodity flow problems with just a few commodities are believed to be much harder than single-commodity maximum-flow or minimum-cost flow problems. In this paper, we describe the first polynomial-time combinatorial algorithms for approximately solving the multicommodity flow problem. The running time of our randomized algorithm is (up to log factors) the same as the time needed to solve k single-commodity flow problems, thus giving the surprising result that approximately computing a k-commodity maximum-flow is not much harder than computing about k single-commodity maximum-flows in isolation. In fact, we prove that a (simple) k-commodity flow problem can be approximately solved by approximately solving O(k log2n) single-commodity minimum-cost flow problems. Our k-commodity algorithm runs in O (knm log4n) time with high probability. We also describe a deterministic algorithm that uses an O(k)-factor more time. Given any multicommodity flow problem as input, both algorithms are guaranteed to provide a feasible solution to a modified flow problem in which all capacities are increased by a (1 + ϵ)-factor, or to provide a proof that there is no feasible solution to the original problem. We also describe faster approximation algorithms for multicommodity flow problems with a special structure, such as those that arise in "sparsest cut" problems and uniform concurrent flow problems.