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        <identifier>oai:drops-oai.dagstuhl.de:18544</identifier>
        <datestamp>2024-03-06T11:02:04Z</datestamp>
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          <dc:title>Solving Irreducible Stochastic Mean-Payoff Games and Entropy Games by Relative Krasnoselskii-Mann Iteration</dc:title>
          <dc:creator>Akian, Marianne</dc:creator>
          <dc:creator>Gaubert, Stéphane</dc:creator>
          <dc:creator>Naepels, Ulysse</dc:creator>
          <dc:creator>Terver, Basile</dc:creator>
          <dc:subject>Stochastic mean-payoff games</dc:subject>
          <dc:subject>concurrent games</dc:subject>
          <dc:subject>entropy games</dc:subject>
          <dc:subject>relative value iteration</dc:subject>
          <dc:subject>Krasnoselskii-Mann fixed point algorithm</dc:subject>
          <dc:subject>Hilbert projective metric</dc:subject>
          <dc:description>We analyse an algorithm solving stochastic mean-payoff games, combining the ideas of relative value iteration and of Krasnoselskii-Mann damping. We derive parameterized complexity bounds for several classes of games satisfying irreducibility conditions. We show in particular that an ε-approximation of the value of an irreducible concurrent stochastic game can be computed in a number of iterations in O(|log(ε)|) where the constant in the O(⋅) is explicit, depending on the smallest non-zero transition probabilities. This should be compared with a bound in O(ε^{-1}|log(ε)|) obtained by Chatterjee and Ibsen-Jensen (ICALP 2014) for the same class of games, and to a O(ε^{-1}) bound by Allamigeon, Gaubert, Katz and Skomra (ICALP 2022) for turn-based games. We also establish parameterized complexity bounds for entropy games, a class of matrix multiplication games introduced by Asarin, Cervelle, Degorre, Dima, Horn and Kozyakin. We derive these results by methods of variational analysis, establishing contraction properties of the relative Krasnoselskii-Mann iteration with respect to Hilbert’s semi-norm.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Marianne Akian and Stéphane Gaubert and Ulysse Naepels and Basile Terver</dc:contributor>
          <dc:date>2023</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 272, 48th International Symposium on Mathematical Foundations of Computer Science (MFCS 2023)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.MFCS.2023.10</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-185448</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2023.10</dc:identifier>
          <dc:language>eng</dc:language>
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