Penalized semidefinite programming for quadratically-constrained quadratic optimization

In this paper, we give a new penalized semidefinite programming approach for non-convex quadratically-constrained quadratic programs (QCQPs). We incorporate penalty terms into the objective of convex relaxations in order to retrieve feasible and near-optimal solutions for non-convex QCQPs. We introd...

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Bibliographic Details
Published in:	Journal of global optimization Vol. 78; no. 3; pp. 423 - 451
Main Authors:	Madani, Ramtin, Kheirandishfard, Mohsen, Lavaei, Javad, Atamtürk, Alper
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.11.2020 Springer Springer Nature B.V
Subjects:	Computer Science Constraints Feasibility Iterative methods Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Quadratic programming Real Functions Semidefinite programming System identification Semidefinite programming Non-convex optimization 65K05 90C22 90-08 Convex relaxation 90C26 Non-linear programming
ISSN:	0925-5001, 1573-2916
Online Access:	Get full text
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