Convergence Analysis of Non-Strongly-Monotone Stochastic Quasi-Variational Inequalities

Fuente: arXiv
Guardado en:
Detalles Bibliográficos
Autores principales: Alizadeh, Zeinab, Jalilzadeh, Afrooz
Formato: Preprint
Publicado: 2024
Materias:
Acceso en línea:
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866917098945511424
author Alizadeh, Zeinab
Jalilzadeh, Afrooz
author_facet Alizadeh, Zeinab
Jalilzadeh, Afrooz
contents While Variational Inequality (VI) is a well-established mathematical framework that subsumes Nash equilibrium and saddle-point problems, less is known about its extension, Quasi-Variational Inequalities (QVI). QVI allows for cases where the constraint set changes as the decision variable varies allowing for a more versatile setting. In this paper, we propose extra-gradient and gradient-based methods for solving a class of monotone Stochastic Quasi-Variational Inequalities (SQVI) and establish a rigorous convergence rate analysis for these methods. Our approach not only advances the theoretical understanding of SQVI but also demonstrates its practical applicability. Specifically, we highlight its effectiveness in reformulating and solving problems such as generalized Nash Equilibrium, bilevel optimization, and saddle-point problems with coupling constraints.
format Preprint
id arxiv_https___arxiv_org_abs_2401_03076
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Convergence Analysis of Non-Strongly-Monotone Stochastic Quasi-Variational Inequalities
Alizadeh, Zeinab
Jalilzadeh, Afrooz
Optimization and Control
While Variational Inequality (VI) is a well-established mathematical framework that subsumes Nash equilibrium and saddle-point problems, less is known about its extension, Quasi-Variational Inequalities (QVI). QVI allows for cases where the constraint set changes as the decision variable varies allowing for a more versatile setting. In this paper, we propose extra-gradient and gradient-based methods for solving a class of monotone Stochastic Quasi-Variational Inequalities (SQVI) and establish a rigorous convergence rate analysis for these methods. Our approach not only advances the theoretical understanding of SQVI but also demonstrates its practical applicability. Specifically, we highlight its effectiveness in reformulating and solving problems such as generalized Nash Equilibrium, bilevel optimization, and saddle-point problems with coupling constraints.
title Convergence Analysis of Non-Strongly-Monotone Stochastic Quasi-Variational Inequalities
topic Optimization and Control
url https://arxiv.org/abs/2401.03076