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Main Authors: Dutta, Joydeep, Lafhim, Lahoussine, Zemkoho, Alain, Zhou, Shenglong
Format: Preprint
Published: 2022
Subjects:
Online Access:https://arxiv.org/abs/2210.02531
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author Dutta, Joydeep
Lafhim, Lahoussine
Zemkoho, Alain
Zhou, Shenglong
author_facet Dutta, Joydeep
Lafhim, Lahoussine
Zemkoho, Alain
Zhou, Shenglong
contents We consider a parametric quasi-variational inequality (QVI) without any convexity assumption. Using the concept of \emph{optimal value function}, we transform the problem into that of solving a nonsmooth system of inequalities. Based on this reformulation, new coderivative estimates as well as robust stability conditions for the optimal solution map of this QVI are developed. Also, for an optimization problem with QVI constraint, necessary optimality conditions are constructed and subsequently, a tailored semismooth Newton-type method is designed, implemented, and tested on a wide range of optimization examples from the literature. In addition to the fact that our approach does not require convexity, its coderivative and stability analysis do not involve second order derivatives, and subsequently, the proposed Newton scheme does not need third order derivatives, as it is the case for some previous works in the literature.
format Preprint
id arxiv_https___arxiv_org_abs_2210_02531
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Nonconvex quasi-variational inequalities: stability analysis and application to numerical optimization
Dutta, Joydeep
Lafhim, Lahoussine
Zemkoho, Alain
Zhou, Shenglong
Optimization and Control
We consider a parametric quasi-variational inequality (QVI) without any convexity assumption. Using the concept of \emph{optimal value function}, we transform the problem into that of solving a nonsmooth system of inequalities. Based on this reformulation, new coderivative estimates as well as robust stability conditions for the optimal solution map of this QVI are developed. Also, for an optimization problem with QVI constraint, necessary optimality conditions are constructed and subsequently, a tailored semismooth Newton-type method is designed, implemented, and tested on a wide range of optimization examples from the literature. In addition to the fact that our approach does not require convexity, its coderivative and stability analysis do not involve second order derivatives, and subsequently, the proposed Newton scheme does not need third order derivatives, as it is the case for some previous works in the literature.
title Nonconvex quasi-variational inequalities: stability analysis and application to numerical optimization
topic Optimization and Control
url https://arxiv.org/abs/2210.02531