| File | |
| Title |
Surrogate duality for robust quasiconvex vector optimization
|
| Creator | |
| Source Title |
Applied Analysis and Optimization
|
| Volume | 2 |
| Issue | 1 |
| Start Page | 27 |
| End Page | 39 |
| Descriptions |
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.
|
| Subjects |
multiobjective programming
robust vector optimization
sur- rogate duality
constraint quali cation
|
| Language |
eng
|
| Resource Type | journal article |
| Date of Issued | 2018 |
| Access Rights | restricted access |
| Relation |
isVersionOf
[URI] http://yokohamapublishers.jp/online2/opaao/vol2/p27.html
isVersionOf
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