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A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach

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arxiv 1901.08511 v4 pith:KTN5TGHG submitted 2019-01-24 math.OC cs.LGstat.ML

Aryan Mokhtari , Asuman Ozdaglar , Sarath Pattathil This is my paper · ORCID
classification math.OCcs.LGstat.ML
keywords pointgradientogdaproblemsproximalsaddlealgorithmsanalysis
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In this paper we consider solving saddle point problems using two variants of Gradient Descent-Ascent algorithms, Extra-gradient (EG) and Optimistic Gradient Descent Ascent (OGDA) methods. We show that both of these algorithms admit a unified analysis as approximations of the classical proximal point method for solving saddle point problems. This viewpoint enables us to develop a new framework for analyzing EG and OGDA for bilinear and strongly convex-strongly concave settings. Moreover, we use the proximal point approximation interpretation to generalize the results for OGDA for a wide range of parameters.

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Cited by 4 Pith papers

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  4. Gradient Methods for Solving Stackelberg Games

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