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