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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Subject | multiobjective programming
robust vector optimization
sur- rogate duality
constraint qualication
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Journal information |
Applied Analysis and Optimization
2
( 1
), 27
- 39
, 2018
|