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  • title: Last-Iterate Convergence in No-regret Learning: Constrained Min-Max Optimization for Convex-Concave Landscapes
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            Last-Iterate Convergence in No-regret Learning: Constrained Min-Max Optimization for Convex-Concave Landscapes
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            Last-Iterate Convergence in No-regret Learning: Constrained Min-Max Optimization for Convex-Concave Landscapes

            Apr 14, 2021

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            Ioannis Panageas

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            QL

            Qi Lei

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            SGN

            Sai Ganesh Nagarajan

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            About

            In a recent series of papers it has been established that variants of Gradient Descent/Ascent and Mirror Descent exhibit last iterate convergence in convex-concave zero-sum games. Specifically, Daskalakis et al 2018, Liang-Stokes 2019, show last iterate convergence of the so called ``Optimistic Gradient Descent/Ascent" for the case of \textit{unconstrained} min-max optimization. Moreover, in Mertikopoulos et al 2019 the authors show that Mirror Descent with an extra gradient step displays last i…

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