Efficient Data Structures for Incremental Exact and Approximate Maximum Flow

Authors Gramoz Goranci , Monika Henzinger



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Author Details

Gramoz Goranci
  • Faculty of Computer Science, Universität Wien, Austria
Monika Henzinger
  • Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria

Acknowledgements

This work was done in part while Gramoz Goranci was at Institute for Theoretical Studies, ETH Zurich, Switzerland. There, he was supported by Dr. Max Rössler, the Walter Haefner Foundation and the ETH Zürich Foundation. We also thank Richard Peng, Thatchaphol Saranurak, Sebastian Forster and Sushant Sachdeva for helpful discussions, and the anonymous reviewers for their insightful comments.

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Gramoz Goranci and Monika Henzinger. Efficient Data Structures for Incremental Exact and Approximate Maximum Flow. In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, pp. 69:1-69:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ICALP.2023.69

Abstract

We show an (1+ε)-approximation algorithm for maintaining maximum s-t flow under m edge insertions in m^{1/2+o(1)} ε^{-1/2} amortized update time for directed, unweighted graphs. This constitutes the first sublinear dynamic maximum flow algorithm in general sparse graphs with arbitrarily good approximation guarantee. Furthermore we give an algorithm that maintains an exact maximum s-t flow under m edge insertions in an n-node graph in Õ(n^{5/2}) total update time. For sufficiently dense graphs, this gives to the first exact incremental algorithm with sub-linear amortized update time for maintaining maximum flows.

Subject Classification

ACM Subject Classification
  • Theory of computation → Dynamic graph algorithms
Keywords
  • dynamic graph algorithms
  • maximum flow
  • data structures

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