Proximal point algorithms for vector DC programming with applications to probabilistic lot sizing with service levels
type: Item
instance of: scholarly article
title: Proximal point algorithms for vector DC programming with applications to probabilistic lot sizing with service levels
zbMATH Open document ID: 1397.90348
DOI: 10.1155/2017/5675183
author: Ying Ji
author: Shaojian Qu
published in: Discrete Dynamics in Nature and Society
publication date: 2018-09-27T00:00:00Z
review text: Summary: We present a new algorithm for solving vector DC programming, where the vector function is a function of the difference of \(C\)-convex functions. Because of the nonconvexity of the objective function, it is difficult to solve this class of problems. We propose several proximal point algorithms to address this class of problems, which make use of the special structure of the problems (i.e., the DC structure). The well-posedness and the global convergence of the proposed algorithms are developed. The efficiency of the proposed algorithm is shown by an application to a multicriteria model stemming from lot sizing problems.
Mathematics Subject Classification ID: 90C29
Mathematics Subject Classification ID: 90B05
Mathematics Subject Classification ID: 90C26
zbMATH DE Number: 6944831
zbMATH Keywords: vector DC programming
zbMATH Keywords: \(C\)-convex functions
zbMATH Keywords: proximal point algorithms
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/5675183
OpenAlex ID: W2766701427
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description: scientific article; zbMATH DE number 6944831