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Continuous Time Learning Algorithms in Optimization and Game Theory

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  • Sylvain Sorin

    (Sorbonne Université, Campus P.&M. Curie)

Abstract

The purpose of this work is the comparison of learning algorithms in continuous time used in optimization and game theory. The first three are issued from no-regret dynamics and cover in particular “Replicator dynamics” and “Local projection dynamics”. Then we study “Conditional gradient” versus “Global projection” dynamics and finally “Frank-Wolfe” versus “Best reply” dynamics. Important similarities occur when considering potential or dissipative games.

Suggested Citation

  • Sylvain Sorin, 2023. "Continuous Time Learning Algorithms in Optimization and Game Theory," Dynamic Games and Applications, Springer, vol. 13(1), pages 3-24, March.
    • Handle: RePEc:spr:dyngam:v:13:y:2023:i:1:d:10.1007_s13235-021-00423-x
    DOI: 10.1007/s13235-021-00423-x
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    References listed on IDEAS

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    Cited by:

    1. Karl D. Lewis & A. J. Shaiju, 2024. "Asymmetric Replicator Dynamics on Polish Spaces: Invariance, Stability, and Convergence," Dynamic Games and Applications, Springer, vol. 14(5), pages 1160-1190, November.

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