Optimality Conditions for Constrained Minimax Optimization

Optimality Conditions for Constrained Minimax Optimization

Year:    2020

Author:    Yu-Hong Dai, Liwei Zhang

CSIAM Transactions on Applied Mathematics, Vol. 1 (2020), Iss. 2 : pp. 296–315

Abstract

Minimax optimization problems arises from both modern machine learning including generative adversarial networks, adversarial training and multi-agent reinforcement learning, as well as from tradition research areas such as saddle point problems, numerical partial differential equations and optimality conditions of equality constrained optimization. For the unconstrained continuous nonconvex-nonconcave situation, Jin, Netrapalli and Jordan (2019) carefully considered the very basic question: what is a proper definition of local optima of a minimax optimization problem, and proposed a proper definition of local optimality called local minimax. We shall extend the definition of local minimax point to constrained nonconvex-nonconcave minimax optimization problems. By analyzing Jacobian uniqueness conditions for the lower-level maximization problem and the strong regularity of Karush-Kuhn-Tucker conditions of the maximization problem, we provide both necessary optimality conditions and sufficient optimality conditions for the local minimax points of constrained minimax optimization problems.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/csiam-am.2020-0014

CSIAM Transactions on Applied Mathematics, Vol. 1 (2020), Iss. 2 : pp. 296–315

Published online:    2020-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    20

Keywords:    Constrained minimax optimization value function Jacobian uniqueness conditions strong regularity necessary optimality conditions sufficient optimality conditions.

Author Details

Yu-Hong Dai

Liwei Zhang

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