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AuthorHamdi, A.
AuthorTaati, A.
AuthorAlmaadeed, T.A.
Available date2023-09-24T07:55:31Z
Publication Date2021
Publication NameRAIRO - Operations Research
ResourceScopus
URIhttp://dx.doi.org/10.1051/ro/2020130
URIhttp://hdl.handle.net/10576/47869
AbstractIn this paper, we study a nonconvex quadratic minimization problem with two quadratic constraints, one of which being convex. We introduce two convex quadratic relaxations (CQRs) and discuss cases, where the problem is equivalent to exactly one of the CQRs. Particularly, we show that the global optimal solution can be recovered from an optimal solution of the CQRs. Through this equivalence, we introduce new conditions under which the problem enjoys strong Lagrangian duality, generalizing the recent condition in the literature. Finally, under the new conditions, we present necessary and sufficient conditions for global optimality of the problem. EDP Sciences, ROADEF, SMAI 2021.
SponsorAcknowledgements. The authors are grateful to the referees for valuable comments and suggestions that helped us improve this article. The authors would like to thank Qatar University for the full support under the Grant NCBP-QUCP-CAS-2020-1.
Languageen
PublisherEDP Sciences
SubjectConvex quadratic relaxation
Quadratically constrained quadratic programming
SDO-relaxation
Strong duality
TitleQuadratic problems with two quadratic constraints: Convex quadratic relaxation and strong lagrangian duality
TypeArticle
PaginationS2905-S2922
Volume Number55


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