Nearly Maximum Flows in Nearly Linear Time

Abstract

We introduce a new approach to the maximum flow problem in undirected, capacitated graphs using $\alpha$-\emph{congestion-approximators}: easy-to-compute functions that approximate the congestion required to route single-commodity demands in a graph to within a factor of $\alpha$. Our algorithm maintains an arbitrary flow that may have some residual excess and deficits, while taking steps to minimize a potential function measuring the congestion of the current flow plus an over-estimate of the congestion required to route the residual demand. Since the residual term over-estimates, the descent process gradually moves the contribution to our potential function from the residual term to the congestion term, eventually achieving a flow routing the desired demands with nearly minimal congestion after $\tilde{O}(α\eps^{-2}\log^2 n)$ iterations. Our approach is similar in spirit to that used by Spielman and Teng (STOC 2004) for solving Laplacian systems, and we summarize our approach as trying to do for ${\ell }_{\infty }$-flows what they do for ${\ell }_2$-flows. Together with a nearly linear time construction of a $n^{o(1)}$-congestion-approximator, we obtain $1+\eps$-optimal single-commodity flows undirected graphs in time $m^{1+o(1)}\eps^{-2}$, yielding the fastest known algorithm for that problem. Our requirements of a congestion-approximator are quite low, suggesting even faster and simpler algorithms for certain classes of graphs. For example, an $\alpha$-competitive oblivious routing tree meets our definition, \emph{even without knowing how to route the tree back in the graph}. For graphs of conductance $\varphi$, a trivial ${\varphi }^{-1}$-congestion-approximator gives an extremely simple algorithm for finding $1+\eps$-optimal-flows in time $\tilde{O}(m{\varphi }^{-1})$.