Strictly convex separable optimization with linear equality constraints and bounded variables

In this paper, minimization problem with a separable strictly convex objective function subject to several linear equality constraints and bounds on the variables (box constraints) is considered. Such problems arise in manufacturing, facility location, resource allocation, engineering and economic a...

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Bibliographic Details
Published inJournal of statistics & management systems Vol. 21; no. 2; pp. 261 - 272
Main Author Stefanov, Stefan M.
Format Journal Article
LanguageEnglish
Published New Delhi Taylor & Francis 04.03.2018
Taru Publications
Subjects
90C25
Convex programming
Engineering and economic applications
Mathematical analysis
Necessary and sufficient condition
Nonlinear programming
Optimization
Resource allocation
Separable programming
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Summary:In this paper, minimization problem with a separable strictly convex objective function subject to several linear equality constraints and bounds on the variables (box constraints) is considered. Such problems arise in manufacturing, facility location, resource allocation, engineering and economic applications, etc. A necessary and sufficient condition (characterization) is proved for a feasible solution to be the (unique) optimal solution of the considered problem. Primal-dual analysis of the proposed approach is made. Examples of some important separable strictly convex objective functions for the problem under consideration are presented.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0972-0510
2169-0014
DOI:10.1080/09720510.2017.1417730