An Infeasible Incremental Bundle Method for Nonsmooth Optimization Problem Based on CVaR Portfolio

For CVaR (conditional value-at-risk) portfolio nonsmooth optimization problem, we propose an infeasible incremental bundle method on the basis of the improvement function and the main idea of incremental method for solving convex finite min-max problems. The presented algorithm only employs the info...

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Main Authors: Jia-Tong Li, Jie Shen, Na Xu
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2021/6640781
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author Jia-Tong Li
Jie Shen
Na Xu
author_facet Jia-Tong Li
Jie Shen
Na Xu
author_sort Jia-Tong Li
collection DOAJ
description For CVaR (conditional value-at-risk) portfolio nonsmooth optimization problem, we propose an infeasible incremental bundle method on the basis of the improvement function and the main idea of incremental method for solving convex finite min-max problems. The presented algorithm only employs the information of the objective function and one component function of constraint functions to form the approximate model for improvement function. By introducing the aggregate technique, we keep the information of previous iterate points that may be deleted from bundle to overcome the difficulty of numerical computation and storage. Our algorithm does not enforce the feasibility of iterate points and the monotonicity of objective function, and the global convergence of the algorithm is established under mild conditions. Compared with the available results, our method loosens the requirements of computing the whole constraint function, which makes the algorithm easier to implement.
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language English
publishDate 2021-01-01
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series Complexity
spelling doaj-art-a4d5686442804c80bbc5e00cd321abc22025-02-03T01:28:43ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66407816640781An Infeasible Incremental Bundle Method for Nonsmooth Optimization Problem Based on CVaR PortfolioJia-Tong Li0Jie Shen1Na Xu2College of Science, Northeast Forestry University, Harbin 150040, ChinaSchool of Mathematics, Liaoning Normal University, Dalian 116029, ChinaSchool of Mathematics, Liaoning Normal University, Dalian 116029, ChinaFor CVaR (conditional value-at-risk) portfolio nonsmooth optimization problem, we propose an infeasible incremental bundle method on the basis of the improvement function and the main idea of incremental method for solving convex finite min-max problems. The presented algorithm only employs the information of the objective function and one component function of constraint functions to form the approximate model for improvement function. By introducing the aggregate technique, we keep the information of previous iterate points that may be deleted from bundle to overcome the difficulty of numerical computation and storage. Our algorithm does not enforce the feasibility of iterate points and the monotonicity of objective function, and the global convergence of the algorithm is established under mild conditions. Compared with the available results, our method loosens the requirements of computing the whole constraint function, which makes the algorithm easier to implement.http://dx.doi.org/10.1155/2021/6640781
spellingShingle Jia-Tong Li
Jie Shen
Na Xu
An Infeasible Incremental Bundle Method for Nonsmooth Optimization Problem Based on CVaR Portfolio
Complexity
title An Infeasible Incremental Bundle Method for Nonsmooth Optimization Problem Based on CVaR Portfolio
title_full An Infeasible Incremental Bundle Method for Nonsmooth Optimization Problem Based on CVaR Portfolio
title_fullStr An Infeasible Incremental Bundle Method for Nonsmooth Optimization Problem Based on CVaR Portfolio
title_full_unstemmed An Infeasible Incremental Bundle Method for Nonsmooth Optimization Problem Based on CVaR Portfolio
title_short An Infeasible Incremental Bundle Method for Nonsmooth Optimization Problem Based on CVaR Portfolio
title_sort infeasible incremental bundle method for nonsmooth optimization problem based on cvar portfolio
url http://dx.doi.org/10.1155/2021/6640781
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