A first-order augmented Lagrangian method for constrained minimax optimization

01/05/2023
by   Zhaosong Lu, et al.
0

In this paper we study a class of constrained minimax problems. In particular, we propose a first-order augmented Lagrangian method for solving them, whose subproblems turn out to be a much simpler structured minimax problem and are suitably solved by a first-order method recently developed in [26] by the authors. Under some suitable assumptions, an operation complexity of O(ε^-4logε^-1), measured by its fundamental operations, is established for the first-order augmented Lagrangian method for finding an ε-KKT solution of the constrained minimax problems.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/04/2023

First-order penalty methods for bilevel optimization

In this paper we study a class of unconstrained and constrained bilevel ...
research
05/31/2018

Minimax Learning for Remote Prediction

The classical problem of supervised learning is to infer an accurate pre...
research
03/27/2018

Iteration-complexity of first-order augmented Lagrangian methods for convex conic programming

In this paper we consider a class of convex conic programming. In partic...
research
06/13/2018

On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization

We study constrained nonconvex optimization problems in machine learning...
research
03/08/2021

Robust and stochastic compliance-based topology optimization with finitely many loading scenarios

In this paper, the problem of load uncertainty in compliance problems is...
research
03/28/2022

Multi-constrained 3D topology optimization via augmented topological level-set

The objective of this paper is to introduce and demonstrate a robust met...
research
08/30/2013

Separable Approximations and Decomposition Methods for the Augmented Lagrangian

In this paper we study decomposition methods based on separable approxim...

Please sign up or login with your details

Forgot password? Click here to reset