ZOMA unifies hybrid zeroth-order estimators, bias corrections (GT/ED/EXTRA), and accelerations (STORM/PAGE/L2S) for decentralized nonconvex PL minimax optimization, claiming convergence rates matching centralized methods plus linear speedup.
Variance-reduced accelerated methods for decentral- ized stochastic double-regularized nonconvex strongly-concave minimax problems
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SGDA-B is the first backtracking-enabled stochastic GDA algorithm for nonconvex-concave minimax problems that achieves the best known complexity bounds among methods agnostic to L, μ, and σ².
PALM achieves Õ(ε^{-1}) first-order complexity for ε-KKT points in convex-strongly-concave minimax problems with functional constraints and Õ(ε^{-3/2}) for the dual in the convex-concave case.
citing papers explorer
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A Unified Zeroth-Order Approach for Decentralized Minimax Optimization
ZOMA unifies hybrid zeroth-order estimators, bias corrections (GT/ED/EXTRA), and accelerations (STORM/PAGE/L2S) for decentralized nonconvex PL minimax optimization, claiming convergence rates matching centralized methods plus linear speedup.
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A Stochastic GDA Method With Backtracking For Solving Nonconvex Concave Minimax Problems
SGDA-B is the first backtracking-enabled stochastic GDA algorithm for nonconvex-concave minimax problems that achieves the best known complexity bounds among methods agnostic to L, μ, and σ².
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First-Order Methods for Solving Convex (Strongly) Concave Minimax Problems with Functional Constraints
PALM achieves Õ(ε^{-1}) first-order complexity for ε-KKT points in convex-strongly-concave minimax problems with functional constraints and Õ(ε^{-3/2}) for the dual in the convex-concave case.