A regularized smoothing Newton-type algorithm for quasi-variational inequalities

作     者：Ni, Tie Zhai, Jun 

作者机构：Liaoning Tech Univ Coll Business Adm Huludao 125105 Peoples R China Univ Chicago Dept Math Chicago IL 60615 USA 

出 版 物：《COMPUTERS & MATHEMATICS WITH APPLICATIONS》 (计算机与数学及其应用)

年 卷 期：2014年第68卷第10期

页      面：1312-1324页

核心收录：

学科分类：08[工学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China 

主　　题：Quasi-variational inequality Non-monotone line search Newton-type algorithm Smoothing function Global convergence Superlinear/quadratic convergence 

摘      要：This paper is concerned with developing an efficient regularized smoothing Newton-type algorithm for quasi-variational inequalities. The proposed algorithm takes the advantage of newly introduced smoothing functions and a non-monotone line search strategy. It is proved to be globally and locally superlinearly/quadratically convergent under suitable assumptions. Numerical results demonstrate that the algorithm generally outperforms the existing interior point method and semismooth method (Facchinei, et al. 2014). (C) 2014 Elsevier Ltd. All rights reserved.