An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue Functions

We present an approximate nonsmooth algorithm to solve a minimization problem, in which the objective function is the sum of a maximum eigenvalue function of matrices and a convex function. The essential idea to solve the optimization problem in this paper is similar to the thought of proximal bundl...

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Main Authors: Wei Wang, Lingling Zhang, Miao Chen, Sida Lin
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2014/893765
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author Wei Wang
Lingling Zhang
Miao Chen
Sida Lin
author_facet Wei Wang
Lingling Zhang
Miao Chen
Sida Lin
author_sort Wei Wang
collection DOAJ
description We present an approximate nonsmooth algorithm to solve a minimization problem, in which the objective function is the sum of a maximum eigenvalue function of matrices and a convex function. The essential idea to solve the optimization problem in this paper is similar to the thought of proximal bundle method, but the difference is that we choose approximate subgradient and function value to construct approximate cutting-plane model to solve the above mentioned problem. An important advantage of the approximate cutting-plane model for objective function is that it is more stable than cutting-plane model. In addition, the approximate proximal bundle method algorithm can be given. Furthermore, the sequences generated by the algorithm converge to the optimal solution of the original problem.
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institution Kabale University
issn 1110-757X
1687-0042
language English
publishDate 2014-01-01
publisher Wiley
record_format Article
series Journal of Applied Mathematics
spelling doaj-art-01b75dff5d62467c90417886795ba6fc2025-02-03T01:07:25ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/893765893765An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue FunctionsWei Wang0Lingling Zhang1Miao Chen2Sida Lin3School of Mathematics, Liaoning Normal University, Liaoning, Dalian 116029, ChinaSchool of Mathematics, Liaoning Normal University, Liaoning, Dalian 116029, ChinaSchool of Mathematics, Liaoning Normal University, Liaoning, Dalian 116029, ChinaSchool of Mathematical Sciences, Dalian University of Technology, Dalian 116024, ChinaWe present an approximate nonsmooth algorithm to solve a minimization problem, in which the objective function is the sum of a maximum eigenvalue function of matrices and a convex function. The essential idea to solve the optimization problem in this paper is similar to the thought of proximal bundle method, but the difference is that we choose approximate subgradient and function value to construct approximate cutting-plane model to solve the above mentioned problem. An important advantage of the approximate cutting-plane model for objective function is that it is more stable than cutting-plane model. In addition, the approximate proximal bundle method algorithm can be given. Furthermore, the sequences generated by the algorithm converge to the optimal solution of the original problem.http://dx.doi.org/10.1155/2014/893765
spellingShingle Wei Wang
Lingling Zhang
Miao Chen
Sida Lin
An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue Functions
Journal of Applied Mathematics
title An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue Functions
title_full An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue Functions
title_fullStr An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue Functions
title_full_unstemmed An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue Functions
title_short An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue Functions
title_sort approximate proximal bundle method to minimize a class of maximum eigenvalue functions
url http://dx.doi.org/10.1155/2014/893765
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