Continuous Time Learning Algorithms in Optimization and Game Theory

Sylvain Sorin

Dynamic Games and Applications, 2023, vol. 13, issue 1, No 2, 3-24

Abstract: 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.

Keywords: Learning algorithms; Continuous time; Optimization; Game theory (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13235-021-00423-x

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