Deterministic Decremental SSSP and Approximate Min-Cost Flow in Almost-Linear Time

TL;DR

This paper presents near-linear time deterministic algorithms for decremental approximate shortest paths and min-cost flow in undirected graphs, advancing the efficiency and removing previous assumptions in these fundamental problems.

Contribution

It introduces a deterministic data structure for approximate decremental SSSP with near-linear total update time, and an almost-linear time algorithm for approximate min-cost flow, removing prior limitations.

Findings

Deterministic data structure for (1+ε)-approximate decremental SSSP in m^{1+o(1)} time.

First almost-linear time algorithm for (1-ε)-approximate min-cost flow in undirected graphs.

Breakthrough in flow-decomposition barrier using randomization.

Abstract

In the decremental single-source shortest paths problem, the goal is to maintain distances from a fixed source $s$ to every vertex $v$ in an $m$-edge graph undergoing edge deletions. In this paper, we conclude a long line of research on this problem by showing a near-optimal deterministic data structure that maintains $(1+\epsilon)$-approximate distance estimates and runs in $m^{1+o(1)}$ total update time. Our result, in particular, removes the oblivious adversary assumption required by the previous breakthrough result by Henzinger et al. [FOCS'14], which leads to our second result: the first almost-linear time algorithm for $(1-\epsilon)$-approximate min-cost flow in undirected graphs where capacities and costs can be taken over edges and vertices. Previously, algorithms for max flow with vertex capacities, or min-cost flow with any capacities required super-linear time. Our result essentially completes the picture for approximate flow in undirected graphs. The key technique of the first result is a novel framework that allows us to treat low-diameter graphs like expanders. This allows us to harness expander properties while bypassing shortcomings of expander decomposition, which almost all previous expander-based algorithms needed to deal with. For the second result, we break the notorious flow-decomposition barrier from the multiplicative-weight-update framework using randomization.