| 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 |
| Journal Identifire |
ISSN 24321656
EISSN 21891664
|
| 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.
|
| Language |
eng
|
| Resource Type | journal article |
| Date of Issued | 2018-05 |
| Rights |
Copyright © 2018 Yokohama Publishers
|
| Publish Type | Accepted Manuscript |
| Access Rights | restricted access |
| Relation |
isVersionOf
[URI] http://yokohamapublishers.jp/online2/opaao/vol2/p27.html
isVersionOf
|
