An accelerated minimax algorithm for convex-concave saddle point problems with nonsmooth coupling function

Author(s)
Radu Ioan Bot, Ernö Robert Csetnek, Michael Sedlmayer
Abstract

In this work we aim to solve a convex-concave saddle point problem, where the convex-concave coupling function is smooth in one variable and nonsmooth in the other and not assumed to be linear in either. The problem is augmented by a nonsmooth regulariser in the smooth component. We propose and investigate a novel algorithm under the name of OGAProx, consisting of an optimistic gradient ascent step in the smooth variable coupled with a proximal step of the regulariser, and which is alternated with a proximal step in the nonsmooth component of the coupling function. We consider the situations convex-concave, convex-strongly concave and strongly convex-strongly concave related to the saddle point problem under investigation. Regarding iterates we obtain (weak) convergence, a convergence rate of order O(1/K ) and linear convergence like O(𝜃^K ) with 𝜃 < 1, respectively. In terms of function values we obtain ergodic convergence rates of order O(1/K), O(1/K^2) and O(𝜃^K) with 𝜃 < 1, respectively. We validate our theoretical considerations on a nonsmooth-linear saddle point problem, the training of multi kernel support vector machines and a classification problem incorporating minimax group fairness.

Organisation(s)
Department of Mathematics, Research Network Data Science
Journal
Computational Optimization and Applications
Volume
86
Pages
925 - 966
No. of pages
42
ISSN
0926-6003
DOI
https://doi.org/10.1007/s10589-022-00378-8
Publication date
2021
Peer reviewed
Yes
Austrian Fields of Science 2012
101016 Optimisation
Keywords
ASJC Scopus subject areas
Computational Mathematics, Control and Optimization, Applied Mathematics
Portal url
https://ucris.univie.ac.at/portal/en/publications/an-accelerated-minimax-algorithm-for-convexconcave-saddle-point-problems-with-nonsmooth-coupling-function(44b4f6c4-9b6f-47db-8350-d393f53def7b).html