AI RESEARCH
Faster Stochastic Algorithms for Minimax Optimization under Polyak--{\L}ojasiewicz Conditions
arXiv CS.LG
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ArXi:2307.15868v2 Announce Type: replace-cross This paper considers stochastic first-order algorithms for minimax optimization under Polyak--{\L}ojasiewicz (PL) conditions. We propose SPIDER-GDA for solving the finite-sum problem of the form $\min_x \max_y f(x,y)\triangleq \frac{1}{n} \sum_{i=1}^n f_i(x,y)$, where the objective function $f(x,y)$ is $\mu_x$-PL in $x$ and $\mu_y$-PL in $y$; and each $f_i(x,y)$ is $L$-smooth.