Path Finding II : An Õ(m sqrt(n)) Algorithm for the Minimum Cost Flow Problem

In this paper we present an $\tilde{O}(m\sqrt{n}\log^{O(1)}U)$ time algorithm for solving the maximum flow problem on directed graphs with $m$ edges, $n$ vertices, and capacity ratio $U$. This improves upon the previous fastest running time of $O(m\min\left(n^{{2/3},m{1/2}\right)\log\left(n{2}/m\right)\log} U)$ achieved over 15 years ago by Goldberg and Rao. In the special case of solving dense directed unit capacity graphs our algorithm improves upon the previous fastest running times of of $O(\min{m^{{3/2},mn{{2/3}}})$} achieved by Even and Tarjan and Karzanov over 35 years ago and of $\tilde{O}(m^{10/7})$ achieved recently by M\k{a}dry. We achieve these results through the development and application of a new general interior point method that we believe is of independent interest. The number of iterations required by this algorithm is better than that predicted by analyzing the best self-concordant barrier of the feasible region. By applying this method to the linear programming formulations of maximum flow, minimum cost flow, and lossy generalized minimum cost flow and applying analysis by Daitch and Spielman we achieve running time of $\tilde{O}(m\sqrt{n}\log^{{O(1)}(U/\epsilon))$} for these problems as well. Furthermore, our algorithm is parallelizable and using a recent nearly linear time work polylogarithmic depth Laplacian system solver of Spielman and Peng we achieve a $\tilde{O}(\sqrt{n}\log^{{O(1)}(U/\epsilon))$} depth algorithm and $\tilde{O}(m\sqrt{n}\log^{{O(1)}(U/\epsilon))$} work algorithm for solving these problems.