Related papers: An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization

URL: http://arxiv.org/abs/2212.02387v4
Date: Tue, 14 May 2024 10:41:02 GMT
Title: An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
Authors: Lesi Chen, Haishan Ye, Luo Luo,
Abstract summary: Decentralized Recursive desc. Method (DREAM) Concretely, it requires $mathcalO(minminappaappa3eps-3,kappa2 N)$ first-order oracle (SFO) calls and $tildemathcalO(kappa2 epsilon-2) communication rounds. Our numerical experiments validate the superiority of previous methods.
Score: 25.00475462213752
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper studies the stochastic nonconvex-strongly-concave minimax optimization over a multi-agent network. We propose an efficient algorithm, called Decentralized Recursive gradient descEnt Ascent Method (DREAM), which achieves the best-known theoretical guarantee for finding the $\epsilon$-stationary points. Concretely, it requires $\mathcal{O}(\min (\kappa^3\epsilon^{-3},\kappa^2 \sqrt{N} \epsilon^{-2} ))$ stochastic first-order oracle (SFO) calls and $\tilde{\mathcal{O}}(\kappa^2 \epsilon^{-2})$ communication rounds, where $\kappa$ is the condition number and $N$ is the total number of individual functions. Our numerical experiments also validate the superiority of DREAM over previous methods.

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