双层优化的一阶惩罚方法

IF 2.6 1区 数学 Q1 MATHEMATICS, APPLIED SIAM Journal on Optimization Pub Date : 2024-06-05 DOI:10.1137/23m1566753
Zhaosong Lu, Sanyou Mei
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引用次数: 0

摘要

SIAM 优化期刊》,第 34 卷第 2 期,第 1937-1969 页,2024 年 6 月。 摘要本文研究了一类无约束和有约束的双层优化问题,其中下层是一个可能的非光滑凸优化问题,而上层是一个可能的非凸优化问题。我们为它们引入了[math]-KKT 解的概念,并证明在适当的假设条件下,[math]-KKT 解会导致基于[math]或[math]-超梯度的静止点。我们还提出了求[math]-KKT 解的一阶惩罚方法,其子问题变成了结构最小问题,可以用作者最近开发的一阶方法适当求解。在适当的假设条件下,所提出的寻找无约束和有约束双级优化问题的 [math]-KKT 解的惩罚方法的运算复杂度分别为 [math] 和 [math],以其基本运算来衡量。我们还给出了初步的数值结果,以说明我们提出的方法的性能。据我们所知,本文是第一部证明双级优化可以近似求解为 minimax 优化的著作,此外,它还为如此复杂的双级优化提供了第一种具有复杂性保证的可实现方法。
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First-Order Penalty Methods for Bilevel Optimization
SIAM Journal on Optimization, Volume 34, Issue 2, Page 1937-1969, June 2024.
Abstract. In this paper, we study a class of unconstrained and constrained bilevel optimization problems in which the lower level is a possibly nonsmooth convex optimization problem, while the upper level is a possibly nonconvex optimization problem. We introduce a notion of [math]-KKT solution for them and show that an [math]-KKT solution leads to an [math]- or [math]-hypergradient–based stationary point under suitable assumptions. We also propose first-order penalty methods for finding an [math]-KKT solution of them, whose subproblems turn out to be a structured minimax problem and can be suitably solved by a first-order method recently developed by the authors. Under suitable assumptions, an operation complexity of [math] and [math], measured by their fundamental operations, is established for the proposed penalty methods for finding an [math]-KKT solution of the unconstrained and constrained bilevel optimization problems, respectively. Preliminary numerical results are presented to illustrate the performance of our proposed methods. To the best of our knowledge, this paper is the first work to demonstrate that bilevel optimization can be approximately solved as minimax optimization, and moreover, it provides the first implementable method with complexity guarantees for such sophisticated bilevel optimization.
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来源期刊
SIAM Journal on Optimization
SIAM Journal on Optimization 数学-应用数学
CiteScore
5.30
自引率
9.70%
发文量
101
审稿时长
6-12 weeks
期刊介绍: The SIAM Journal on Optimization contains research articles on the theory and practice of optimization. The areas addressed include linear and quadratic programming, convex programming, nonlinear programming, complementarity problems, stochastic optimization, combinatorial optimization, integer programming, and convex, nonsmooth and variational analysis. Contributions may emphasize optimization theory, algorithms, software, computational practice, applications, or the links between these subjects.
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