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Documents authored by Zampetakis, Manolis

Document
DOI: 10.4230/LIPIcs.ITCS.2024.5
On the Complexity of Computing Sparse Equilibria and Lower Bounds for No-Regret Learning in Games

Authors: Ioannis Anagnostides, Alkis Kalavasis, Tuomas Sandholm, and Manolis Zampetakis

Published in: LIPIcs, Volume 287, 15th Innovations in Theoretical Computer Science Conference (ITCS 2024)

Abstract
Characterizing the performance of no-regret dynamics in multi-player games is a foundational problem at the interface of online learning and game theory. Recent results have revealed that when all players adopt specific learning algorithms, it is possible to improve exponentially over what is predicted by the overly pessimistic no-regret framework in the traditional adversarial regime, thereby leading to faster convergence to the set of coarse correlated equilibria (CCE) - a standard game-theoretic equilibrium concept. Yet, despite considerable recent progress, the fundamental complexity barriers for learning in normal- and extensive-form games are poorly understood. In this paper, we make a step towards closing this gap by first showing that - barring major complexity breakthroughs - any polynomial-time learning algorithms in extensive-form games need at least 2^{log^{1/2 - o(1)} |𝒯|} iterations for the average regret to reach below even an absolute constant, where |𝒯| is the number of nodes in the game. This establishes a superpolynomial separation between no-regret learning in normal- and extensive-form games, as in the former class a logarithmic number of iterations suffices to achieve constant average regret. Furthermore, our results imply that algorithms such as multiplicative weights update, as well as its optimistic counterpart, require at least 2^{(log log m)^{1/2 - o(1)}} iterations to attain an O(1)-CCE in m-action normal-form games under any parameterization. These are the first non-trivial - and dimension-dependent - lower bounds in that setting for the most well-studied algorithms in the literature. From a technical standpoint, we follow a beautiful connection recently made by Foster, Golowich, and Kakade (ICML '23) between sparse CCE and Nash equilibria in the context of Markov games. Consequently, our lower bounds rule out polynomial-time algorithms well beyond the traditional online learning framework, capturing techniques commonly used for accelerating centralized equilibrium computation.
Cite as

Ioannis Anagnostides, Alkis Kalavasis, Tuomas Sandholm, and Manolis Zampetakis. On the Complexity of Computing Sparse Equilibria and Lower Bounds for No-Regret Learning in Games. In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 5:1-5:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{anagnostides_et_al:LIPIcs.ITCS.2024.5,
  author =	{Anagnostides, Ioannis and Kalavasis, Alkis and Sandholm, Tuomas and Zampetakis, Manolis},
  title =	{{On the Complexity of Computing Sparse Equilibria and Lower Bounds for No-Regret Learning in Games}},
  booktitle =	{15th Innovations in Theoretical Computer Science Conference (ITCS 2024)},
  pages =	{5:1--5:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-309-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{287},
  editor =	{Guruswami, Venkatesan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2024.5},
  URN =		{urn:nbn:de:0030-drops-195334},
  doi =		{10.4230/LIPIcs.ITCS.2024.5},
  annote =	{Keywords: No-regret learning, extensive-form games, multiplicative weights update, optimism, lower bounds}
}
Document
DOI: 10.4230/LIPIcs.CCC.2020.19
On the Complexity of Modulo-q Arguments and the Chevalley - Warning Theorem

Authors: Mika Göös, Pritish Kamath, Katerina Sotiraki, and Manolis Zampetakis

Published in: LIPIcs, Volume 169, 35th Computational Complexity Conference (CCC 2020)

Abstract
We study the search problem class PPA_q defined as a modulo-q analog of the well-known polynomial parity argument class PPA introduced by Papadimitriou (JCSS 1994). Our first result shows that this class can be characterized in terms of PPA_p for prime p. Our main result is to establish that an explicit version of a search problem associated to the Chevalley - Warning theorem is complete for PPA_p for prime p. This problem is natural in that it does not explicitly involve circuits as part of the input. It is the first such complete problem for PPA_p when p ≥ 3. Finally we discuss connections between Chevalley-Warning theorem and the well-studied short integer solution problem and survey the structural properties of PPA_q.
Cite as

Mika Göös, Pritish Kamath, Katerina Sotiraki, and Manolis Zampetakis. On the Complexity of Modulo-q Arguments and the Chevalley - Warning Theorem. In 35th Computational Complexity Conference (CCC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 169, pp. 19:1-19:42, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{goos_et_al:LIPIcs.CCC.2020.19,
  author =	{G\"{o}\"{o}s, Mika and Kamath, Pritish and Sotiraki, Katerina and Zampetakis, Manolis},
  title =	{{On the Complexity of Modulo-q Arguments and the Chevalley - Warning Theorem}},
  booktitle =	{35th Computational Complexity Conference (CCC 2020)},
  pages =	{19:1--19:42},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-156-6},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{169},
  editor =	{Saraf, Shubhangi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CCC.2020.19},
  URN =		{urn:nbn:de:0030-drops-125712},
  doi =		{10.4230/LIPIcs.CCC.2020.19},
  annote =	{Keywords: Total NP Search Problems, Modulo-q arguments, Chevalley - Warning Theorem}
}
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