Nonconvex quasi-variational inequalities: stability analysis and application to numerical optimization

Autor:	Dutta, Joydeep, Lafhim, Lahoussine, Zemkoho, Alain, Zhou, Shenglong
Rok vydání:	2022
Předmět:	Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 figures, 4 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.02531 Zobrazit plný text záznamu View this record from Arxiv