The rate of convergence of Bregman proximal methods: Local geometry vs. regularity vs. sharpness - POLARIS - Performance analysis and Optimization of LARge Infrastructure and Systems Access content directly
Preprints, Working Papers, ... Year : 2023

The rate of convergence of Bregman proximal methods: Local geometry vs. regularity vs. sharpness

Abstract

We examine the last-iterate convergence rate of Bregman proximal methods - from mirror descent to mirror-prox and its optimistic variants - as a function of the local geometry induced by the prox-mapping defining the method. For generality, we focus on local solutions of constrained, non-monotone variational inequalities, and we show that the convergence rate of a given method depends sharply on its associated Legendre exponent, a notion that measures the growth rate of the underlying Bregman function (Euclidean, entropic, or other) near a solution. In particular, we show that boundary solutions exhibit a stark separation of regimes between methods with a zero and non-zero Legendre exponent: the former converge at a linear rate, while the latter converge, in general, sublinearly. This dichotomy becomes even more pronounced in linearly constrained problems where methods with entropic regularization achieve a linear convergence rate along sharp directions, compared to convergence in a finite number of steps under Euclidean regularization.
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hal-04307796 , version 1 (26-11-2023)

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Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos. The rate of convergence of Bregman proximal methods: Local geometry vs. regularity vs. sharpness. 2023. ⟨hal-04307796⟩
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