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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.
Journal Title
Applied Analysis and Optimization
Volume
2
Issue
1
Start Page
27
End Page
39
ISSN
24321656
ISSN(Online)
21891664
Published Date
2018-05
NII Type
Journal Article
Format
PDF
Resource URL(IsVersionOf)
http://yokohamapublishers.jp/online2/opaao/vol2/p27.html
Rights
Copyright © 2018 Yokohama Publishers
Text Version
著者版
OAI-PMH Set
Interdisciplinary Graduate School of Science and Engineering