Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/170
Title: Lagrange multiplier necessary conditions for global optimality for non-convex minimization over a quadratic constraint via S-lemma
Authors: Jeyakumar, V
Srisatkunarajah, S
Keywords: Difference of quadratic and convex functions;Fractional programs;Global optimality;Lagrange multipliers;Single quadratic constraint;Smooth non-convex minimization
Issue Date: Jan-2009
Publisher: Springer-Verlag
Abstract: In this paper, we present Lagrange multiplier necessary conditions for global optimality that apply to non-convex optimization problems beyond quadratic optimization problems subject to a single quadratic constraint. In particular, we show that our optimality conditions apply to problems where the objective function is the difference of quadratic and convex functions over a quadratic constraint, and to certain class of fractional programming problems. Our necessary conditions become necessary and sufficient conditions for global optimality for quadratic minimization subject to quadratic constraint. As an application, we also obtain global optimality conditions for a class of trust-region problems. Our approach makes use of outer-estimators, and the powerful S-lemma which has played key role in control theory and semidefinite optimization. We discuss numerical examples to illustrate the significance of our optimality conditions.
URI: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/170
ISSN: 18624472
Appears in Collections:Mathematics and Statistics

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