Web: http://arxiv.org/abs/2206.08573

June 20, 2022, 1:10 a.m. | Simon S. Du, Gauthier Gidel, Michael I. Jordan, Chris Junchi Li

cs.LG updates on arXiv.org arxiv.org

We consider the smooth convex-concave bilinearly-coupled saddle-point
problem, $\min_{\mathbf{x}}\max_{\mathbf{y}}~F(\mathbf{x}) +
H(\mathbf{x},\mathbf{y}) - G(\mathbf{y})$, where one has access to stochastic
first-order oracles for $F$, $G$ as well as the bilinear coupling function $H$.
Building upon standard stochastic extragradient analysis for variational
inequalities, we present a stochastic \emph{accelerated gradient-extragradient
(AG-EG)} descent-ascent algorithm that combines extragradient and Nesterov's
acceleration in general stochastic settings. This algorithm leverages scheduled
restarting to admit a fine-grained nonasymptotic convergence rate that matches
known lower bounds by both \citet{ibrahim2020linear} …

arxiv math optimization

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