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Documents authored by Shepherd, F. Bruce

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APPROX
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2026.12
Uncrossed Multiflows and Applications to Disjoint Paths

Authors: Chandra Chekuri, Guyslain Naves, Joseph Poremba, and F. Bruce Shepherd

Published in: LIPIcs, Volume 392, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2026)

Abstract
A multiflow in a planar graph is uncrossed if the curves identified by its support paths do not cross in the plane. Recently, uncrossed flows have played a role in approximation algorithms for maximum disjoint paths in "fully-planar" instances, where the combined supply-plus-demand graph is planar. They are also used in algorithms to find low-congestion unsplittable flows for both fully-planar and single-source instances. For these two instance classes, any fractional multiflow can be converted into one that is uncrossed, which these algorithms then exploit to obtain their results. We investigate the utility of uncrossed flow more generally and ask three key questions. First, are there other interesting planar multiflow instances that admit uncrossed flows (beyond fully-planar and single-source)? We answer affirmatively, demonstrating a new family of "pairwise-planar" instances whose fractional flows can be uncrossed. This family subsumes fully-planar but includes substantially more, such as (2-connected) fully-compliant series-parallel instances and some instances that have large clique demand graphs. Second, can we always round a fractional uncrossed flow to a "good" integral flow? We again answer positively. For maximization problems, we show any fractional uncrossed flow can be rounded to an integral flow with a constant fraction of its value. For congestion problems (where we must fully route all given demands), we give a rounding procedure that yields an integral multiflow with edge congestion 2. Consequently, we obtain constant-factor approximation algorithms for maximum disjoint paths and minimum congestion integer multiflow for pairwise-planar instances, and show such instances have a constant integral flow-multicut gap. Finally we ask, given an arbitrary planar instance, can we determine if there exists a congestion-1 uncrossed fractional flow (congestion setting) or find the maximum value uncrossed fractional flow (maximization setting)? For congestion, we show this problem is NP-hard, but finding uncrossed edge-disjoint paths is polytime solvable if the demands span a bounded number of faces. For maximization, we present a strong (almost-polynomial) inapproximability result.
Cite as

Chandra Chekuri, Guyslain Naves, Joseph Poremba, and F. Bruce Shepherd. Uncrossed Multiflows and Applications to Disjoint Paths. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 392, pp. 12:1-12:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chekuri_et_al:LIPIcs.APPROX/RANDOM.2026.12,
  author =	{Chekuri, Chandra and Naves, Guyslain and Poremba, Joseph and Shepherd, F. Bruce},
  title =	{{Uncrossed Multiflows and Applications to Disjoint Paths}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2026)},
  pages =	{12:1--12:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-449-9},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{392},
  editor =	{Singh, Mohit and Gur, Tom},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2026.12},
  URN =		{urn:nbn:de:0030-drops-277298},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2026.12},
  annote =	{Keywords: Network Flows, Disjoint Paths, Planar Graphs, Crossing, Flow-Multicut Gap, Approximation Algorithms, Combinatorial Optimization}
}
Document
DOI: 10.4230/LIPIcs.APPROX-RANDOM.2018.23
Multi-Agent Submodular Optimization

Authors: Richard Santiago and F. Bruce Shepherd

Published in: LIPIcs, Volume 116, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2018)

Abstract
Recent years have seen many algorithmic advances in the area of submodular optimization: (SO) min/max~f(S): S in F, where F is a given family of feasible sets over a ground set V and f:2^V - > R is submodular. This progress has been coupled with a wealth of new applications for these models. Our focus is on a more general class of multi-agent submodular optimization (MASO) min/max Sum_{i=1}^{k} f_i(S_i): S_1 u+ S_2 u+ ... u+ S_k in F. Here we use u+ to denote disjoint union and hence this model is attractive where resources are being allocated across k agents, each with its own submodular cost function f_i(). This was introduced in the minimization setting by Goel et al. In this paper we explore the extent to which the approximability of the multi-agent problems are linked to their single-agent versions, referred to informally as the multi-agent gap. We present different reductions that transform a multi-agent problem into a single-agent one. For minimization, we show that (MASO) has an O(alpha * min{k, log^2 (n)})-approximation whenever (SO) admits an alpha-approximation over the convex formulation. In addition, we discuss the class of "bounded blocker" families where there is a provably tight O(log n) multi-agent gap between (MASO) and (SO). For maximization, we show that monotone (resp. nonmonotone) (MASO) admits an alpha (1-1/e) (resp. alpha * 0.385) approximation whenever monotone (resp. nonmonotone) (SO) admits an alpha-approximation over the multilinear formulation; and the 1-1/e multi-agent gap for monotone objectives is tight. We also discuss several families (such as spanning trees, matroids, and p-systems) that have an (optimal) multi-agent gap of 1. These results substantially expand the family of tractable models for submodular maximization.
Cite as

Richard Santiago and F. Bruce Shepherd. Multi-Agent Submodular Optimization. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 116, pp. 23:1-23:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{santiago_et_al:LIPIcs.APPROX-RANDOM.2018.23,
  author =	{Santiago, Richard and Shepherd, F. Bruce},
  title =	{{Multi-Agent Submodular Optimization}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2018)},
  pages =	{23:1--23:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-085-9},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{116},
  editor =	{Blais, Eric and Jansen, Klaus and D. P. Rolim, Jos\'{e} and Steurer, David},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2018.23},
  URN =		{urn:nbn:de:0030-drops-94276},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2018.23},
  annote =	{Keywords: submodular optimization, multi-agent, approximation algorithms}
}

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