Optimal Algorithms for Decentralized Stochastic Variational Inequalities

• URL: http://arxiv.org/abs/2202.02771v2
• Date: Sun, 2 Apr 2023 11:46:07 GMT
• Title: Optimal Algorithms for Decentralized Stochastic Variational Inequalities
• Authors: Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richt\'arik, Alexander Gasnikov
• Abstract summary: This work concentrates on the decentralized setting, which is increasingly important but not well understood. We present lower bounds for both communication and local iterations and construct optimal algorithms that match these lower bounds. Our algorithms are the best among the available not only in the decentralized case, but also in the deterministic and non-distributed literature.
• Score: 113.43047601775453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational inequalities are a formalism that includes games, minimization, saddle point, and equilibrium problems as special cases. Methods for variational inequalities are therefore universal approaches for many applied tasks, including machine learning problems. This work concentrates on the decentralized setting, which is increasingly important but not well understood. In particular, we consider decentralized stochastic (sum-type) variational inequalities over fixed and time-varying networks. We present lower complexity bounds for both communication and local iterations and construct optimal algorithms that match these lower bounds. Our algorithms are the best among the available literature not only in the decentralized stochastic case, but also in the decentralized deterministic and non-distributed stochastic cases. Experimental results confirm the effectiveness of the presented algorithms.

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