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An Efficient Semi-Streaming PTAS for Tournament Feedback Arc Set with Few Passes

Authors Anubhav Baweja, Justin Jia, David P. Woodruff

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Author Details

Anubhav Baweja
  • Carnegie Mellon University, Pittsburgh, PA, USA
Justin Jia
  • Carnegie Mellon University, Pittsburgh, PA, USA
David P. Woodruff
  • Carnegie Mellon University, Pittsburgh, PA, USA

Funding

  • Woodruff, David P.: partial support from NSF grant No. CCF-181584 and a Simons Investigator Award.

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Anubhav Baweja, Justin Jia, and David P. Woodruff. An Efficient Semi-Streaming PTAS for Tournament Feedback Arc Set with Few Passes. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 16:1-16:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.ITCS.2022.16

Abstract

We present the first semi-streaming polynomial-time approximation scheme (PTAS) for the minimum feedback arc set problem on directed tournaments in a small number of passes. Namely, we obtain a (1 + ε)-approximation in time O (poly(n) 2^{poly(1/ε)}), with p passes, in n^{1+1/p} ⋅ poly((log n)/ε) space. The only previous algorithm with this pass/space trade-off gave a 3-approximation (SODA, 2020), and other polynomial-time algorithms which achieved a (1+ε)-approximation did so with quadratic memory or with a linear number of passes. We also present a new time/space trade-off for 1-pass algorithms that solve the tournament feedback arc set problem. This problem has several applications in machine learning such as creating linear classifiers and doing Bayesian inference. We also provide several additional algorithms and lower bounds for related streaming problems on directed graphs, which is a largely unexplored territory.

Subject Classification

ACM Subject Classification
  • Theory of computation → Sketching and sampling
Keywords
  • Minimum Feedback Arc Set
  • Tournament Graphs
  • Approximation Algorithms
  • Semi-streaming Algorithms

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