A simple deterministic near-linear time approximation scheme for transshipment with arbitrary positive edge costs

TL;DR

This paper presents a simple, deterministic near-linear time approximation algorithm for the uncapacitated minimum cost flow problem in undirected graphs, improving efficiency and removing reliance on randomization compared to prior methods.

Contribution

It introduces a deterministic near-linear time approximation scheme for transshipment that does not depend on demands or edge costs, unlike previous randomized approaches.

Findings

Algorithm runs in $O(rac{1}{ ext{ε}^2} m ext{polylog} n)$ time.

Produces flows within $(1 + ext{ε})$ of optimal cost.

Deterministically approximates routing decisions without random embeddings.

Abstract

We describe a simple deterministic near-linear time approximation scheme for uncapacitated minimum cost flow in undirected graphs with real edge weights, a problem also known as transshipment. Specifically, our algorithm takes as input a (connected) undirected graph $G = (V, E)$, vertex demands $b \in \mathbb{R}^V$ such that $\sum_{v \in V} b(v) = 0$, positive edge costs $c \in \mathbb{R}_{>0}^E$, and a parameter $\varepsilon > 0$. In $O(\varepsilon^{-2} m \log^{O(1)} n)$ time, it returns a flow $f$ such that the net flow out of each vertex is equal to the vertex's demand and the cost of the flow is within a $(1 + \varepsilon)$ factor of optimal. Our algorithm is combinatorial and has no running time dependency on the demands or edge costs. With the exception of a recent result presented at STOC 2022 for polynomially bounded edge weights, all almost- and near-linear time approximation schemes for transshipment relied on randomization to embed the problem instance into low-dimensional space. Our algorithm instead deterministically approximates the cost of routing decisions that would be made if the input were subject to a random tree embedding. To avoid computing the $\Omega(n^2)$ vertex-vertex distances that an approximation of this kind suggests, we also take advantage of the clustering method used in the well-known Thorup-Zwick distance oracle.