A Unifying Approach to Robust Convex Infinite Optimization Duality.
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| Title: | A Unifying Approach to Robust Convex Infinite Optimization Duality. |
|---|---|
| Authors: | Dinh, Nguyen1 ndinh@hcmiu.edu.vn, Goberna, Miguel2 mgoberna@ua.es, López, Marco marco.antonio@ua.es, Volle, Michel3 michel.volle@univ-avignon.fr |
| Source: | Journal of Optimization Theory & Applications. Sep2017, Vol. 174 Issue 3, p650-685. 36p. |
| Subjects: | Robust convex optimization, Mathematical optimization, Infinity (Mathematics), Convex domains, Problem solving |
| Abstract: | This paper considers an uncertain convex optimization problem, posed in a locally convex decision space with an arbitrary number of uncertain constraints. To this problem, where the uncertainty only affects the constraints, we associate a robust (pessimistic) counterpart and several dual problems. The paper provides corresponding dual variational principles for the robust counterpart in terms of the closed convexity of different associated cones. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Optimization Theory & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: A Unifying Approach to Robust Convex Infinite Optimization Duality. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dinh%2C+Nguyen%22">Dinh, Nguyen</searchLink><relatesTo>1</relatesTo><i> ndinh@hcmiu.edu.vn</i><br /><searchLink fieldCode="AR" term="%22Goberna%2C+Miguel%22">Goberna, Miguel</searchLink><relatesTo>2</relatesTo><i> mgoberna@ua.es</i><br /><searchLink fieldCode="AR" term="%22López%2C+Marco%22">López, Marco</searchLink><i> marco.antonio@ua.es</i><br /><searchLink fieldCode="AR" term="%22Volle%2C+Michel%22">Volle, Michel</searchLink><relatesTo>3</relatesTo><i> michel.volle@univ-avignon.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Optimization+Theory+%26+Applications%22">Journal of Optimization Theory & Applications</searchLink>. Sep2017, Vol. 174 Issue 3, p650-685. 36p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Robust+convex+optimization%22">Robust convex optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Infinity+%28Mathematics%29%22">Infinity (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Convex+domains%22">Convex domains</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper considers an uncertain convex optimization problem, posed in a locally convex decision space with an arbitrary number of uncertain constraints. To this problem, where the uncertainty only affects the constraints, we associate a robust (pessimistic) counterpart and several dual problems. The paper provides corresponding dual variational principles for the robust counterpart in terms of the closed convexity of different associated cones. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Optimization Theory & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10957-017-1136-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 36 StartPage: 650 Subjects: – SubjectFull: Robust convex optimization Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Infinity (Mathematics) Type: general – SubjectFull: Convex domains Type: general – SubjectFull: Problem solving Type: general Titles: – TitleFull: A Unifying Approach to Robust Convex Infinite Optimization Duality. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dinh, Nguyen – PersonEntity: Name: NameFull: Goberna, Miguel – PersonEntity: Name: NameFull: López, Marco – PersonEntity: Name: NameFull: Volle, Michel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 00223239 Numbering: – Type: volume Value: 174 – Type: issue Value: 3 Titles: – TitleFull: Journal of Optimization Theory & Applications Type: main |
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