A smoothing Levenberg–Marquardt method for generalized semi-infinite programming

Autor:	Weiai Liu, Changyu Wang
Rok vydání:	2013
Předmět:	Levenberg–Marquardt algorithm Computational Mathematics Mathematical optimization Karush–Kuhn–Tucker conditions Applied Mathematics Numerical analysis Superlinear convergence Convergence (routing) Generalized semi-infinite programming Function (mathematics) Smoothing Mathematics
Zdroj:	Computational and Applied Mathematics. 32:89-105
ISSN:	1807-0302 0101-8205
Popis:	This paper is concerned with a numerical method for generalized semi-infinite programming problem. We first reformulate the generalized semi-infinite programming problem into an invariable-dimensional KKT system. Then by using perturbed Fischer–Burmeister function, we present a smoothing Levenberg–Marquardt method for solving this system of semismooth equations and show its global convergence under common conditions. Furthermore, the local superlinear convergence of the method is proved under local error bound condition. Finally, numerical results are given.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c80e151f0c3c95dcb3df68a51bcca4a6 https://doi.org/10.1007/s40314-013-0013-y Zobrazit plný text záznamu