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language
eng
Author
Lee, Gue Myung
Description
Robust optimization problems, which have uncertain data, are considered. We prove surrogate duality theorems for robust quasiconvex optimization problems and surrogate min-max duality theorems for robust convex opti-mization problems. We give necessary and sufficient constraint qualifications for surrogate duality and surrogate min-max duality, and show some exam-ples at which such duality results are used effectively. Moreover, we obtain a surrogate duality theorem and a surrogate min-max duality theorem for semi-definite optimization problems in the face of data uncertainty.
Subject
Nonlinear programming
quasiconvex programming
robust optimization
Journal Title
European Journal of Operational Research
Volume
231
Issue
2
Start Page
257
End Page
262
ISSN
03772217
Published Date
2013-12-01
DOI
DOI Date
2015-07-14
NCID
AA0017802X
NII Type
Journal Article
Format
PDF
Text Version
著者版
OAI-PMH Set
Interdisciplinary Graduate School of Science and Engineering
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