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Maximum Matching in Two, Three, and a Few More Passes Over Graph Streams Kale, Sagar Tirodkar, Sumedh Semi Streaming Maximum Matching We consider the maximum matching problem in the semi-streaming model formalized by Feigenbaum, Kannan, McGregor, Suri, and Zhang that is inspired by giant graphs of today. As our main result, we give a two-pass (1/2 + 1/16)-approximation algorithm for triangle-free graphs and a two-pass (1/2 + 1/32)-approximation algorithm for general graphs; these improve the approximation ratios of 1/2 + 1/52 for bipartite graphs and 1/2 + 1/140 for general graphs by Konrad, Magniez, and Mathieu. In three passes, we achieve approximation ratios of 1/2 + 1/10 for triangle-free graphs and 1/2 + 1/19.753 for general graphs. We also give a multi-pass algorithm where we bound the number of passes precisely - we give a (2/3 - epsilon)-approximation algorithm that uses 2/(3 epsilon) passes for triangle-free graphs and 4/(3 epsilon) passes for general graphs. Our algorithms are simple and combinatorial, use O(n log(n)) space, and have O(1) update time per edge. For general graphs, our multi-pass algorithm improves the best known deterministic algorithms in terms of the number of passes: * Ahn and Guha give a (2/3 - epsilon)-approximation algorithm that uses O(log(1/epsilon)/epsilon^2) passes, whereas our (2/3 - epsilon)-approximation algorithm uses 4/(epsilon) passes; * they also give a (1 - epsilon)-approximation algorithm that uses O(log(n) poly(1/epsilon)) passes, where n is the number of vertices of the input graph; although our algorithm is (2/3 - epsilon)-approximation, our number of passes do not depend on n. Earlier multi-pass algorithms either have a large constant inside big-O notation for the number of passes or the constant cannot be determined due to the involved analysis, so our multi-pass algorithm should use much fewer passes for approximation ratios bounded slightly below 2/3. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Sagar Kale and Sumedh Tirodkar 2017 Is Part Of LIPIcs, Volume 81, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2017) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.APPROX-RANDOM.2017.15 urn:nbn:de:0030-drops-75645 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2017.15 eng https://creativecommons.org/licenses/by/3.0/legalcode