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language
eng
Author
Suzuki, Satoshi Department of Mathematics, Interdisciplinary Graduate School of Science and Engineering, Shimane University
Description
In this paper, we study quasiconvex vector optimization with data uncertainty via robust optimization. By using scalarization, we introduce two types of surrogate duality theorems for robust quasiconvex vector optimization. We show surrogate min-max duality theorems for quasiconvex vector optimization with uncertain objective and/or constraints. For the problem with uncertain objective, we introduce its robust counterpart as a set-valued optimization problem.
Subject
multiobjective programming
robust vector optimization
sur- rogate duality
constraint quali cation
Journal Title
Applied Analysis and Optimization
Volume
2
Issue
1
Start Page
27
End Page
39
Published Date
2018
NII Type
Journal Article
Resource URL(IsVersionOf)
http://www.ybook.co.jp/online2/opaao/vol2/p27.html
OAI-PMH Set
Interdisciplinary Graduate School of Science and Engineering
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