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https://doi.org/10.1109/focs57...
Article . 2023 . Peer-reviewed
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Article . 2022
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A deterministic near-linear time approximation scheme for geometric transportation

Authors: Fox, Emily; Lu, Jiashuai;

A deterministic near-linear time approximation scheme for geometric transportation

Abstract

Given a set of points $P = (P^+ \sqcup P^-) \subset \mathbb{R}^d$ for some constant $d$ and a supply function $μ:P\to \mathbb{R}$ such that $μ(p) > 0~\forall p \in P^+$, $μ(p) < 0~\forall p \in P^-$, and $\sum_{p\in P}{μ(p)} = 0$, the geometric transportation problem asks one to find a transportation map $τ: P^+\times P^-\to \mathbb{R}_{\ge 0}$ such that $\sum_{q\in P^-}{τ(p, q)} = μ(p)~\forall p \in P^+$, $\sum_{p\in P^+}{τ(p, q)} = -μ(q)~ \forall q \in P^-$, and the weighted sum of Euclidean distances for the pairs $\sum_{(p,q)\in P^+\times P^-}τ(p, q)\cdot ||q-p||_2$ is minimized. We present the first deterministic algorithm that computes, in near-linear time, a transportation map whose cost is within a $(1 + \varepsilon)$ factor of optimal. More precisely, our algorithm runs in $O(n\varepsilon^{-(d+2)}\log^5{n}\log{\log{n}})$ time for any constant $\varepsilon > 0$. Surprisingly, our result is not only a generalization of a bipartite matching one to arbitrary instances of geometric transportation, but it also reduces the running time for all previously known $(1 + \varepsilon)$-approximation algorithms, randomized or deterministic, even for geometric bipartite matching. In particular, we give the first $(1 + \varepsilon)$-approximate deterministic algorithm for geometric bipartite matching and the first $(1 + \varepsilon)$-approximate deterministic or randomized algorithm for geometric transportation with no dependence on $d$ in the exponent of the running time's polylog. As an additional application of our main ideas, we also give the first randomized near-linear $O(\varepsilon^{-2} m \log^{O(1)} n)$ time $(1 + \varepsilon)$-approximation algorithm for the uncapacitated minimum cost flow (transshipment) problem in undirected graphs with arbitrary real edge costs.

To appear in FOCS 2023. 24 pages. Update 2: Added corrections for minimum cost flow approximation scheme. Addressed reviewer comments. Update 1: Adds a new randomized near-linear time approximation scheme for uncapacitated minimum cost flow in undirected graphs (transshipment) with arbitrary edge costs. References more recent work in geometric bipartite matching

Related Organizations
Keywords

Computational Geometry (cs.CG), FOS: Computer and information sciences, Computer Science - Data Structures and Algorithms, Computer Science - Computational Geometry, Data Structures and Algorithms (cs.DS)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
Green