New Algorithms and Lower Bounds for All-Pairs Max-Flow in Undirected Graphs
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We investigate the time-complexity of the All-Pairs Max-Flow problem: Given a graph with n nodes and m edges, compute for all pairs of nodes the maximum-flow value between them. If Max-Flow (the version with a given source-sink pair s,t) can be solved in time T(m), then an O(n^2) · T(m) is a trivial upper bound. But can we do better? For directed graphs, recent results in fine-grained complexity suggest that this time bound is essentially optimal. In contrast, for undirected graphs with edge capacities, a seminal algorithm of Gomory and Hu (1961) runs in much faster time O(n)· T(m). Under the plausible assumption that Max-Flow can be solved in near-linear time m^1+o(1), this half-century old algorithm yields an nm^1+o(1) bound. Several other algorithms have been designed through the years, including Õ(mn) time for unit-capacity edges (unconditionally), but none of them break the O(mn) barrier. Meanwhile, no super-linear lower bound was shown for undirected graphs. We design the first hardness reductions for All-Pairs Max-Flow in undirected graphs, giving an essentially optimal lower bound for the node-capacities setting. For edge capacities, our efforts to prove similar lower bounds have failed, but we have discovered a surprising new algorithm that breaks the O(mn) barrier for graphs with unit-capacity edges! Assuming T(m)=m^1+o(1), our algorithm runs in time m^3/2 +o(1) and outputs a cut-equivalent tree (similarly to the Gomory-Hu algorithm). Even with current Max-Flow algorithms we improve state-of-the-art in many density regimes.
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