Maximization of a convex quadratic function under linear constraints

Konno, H. (1976). Maximization of a convex quadratic function under linear constraints. Mathematical Programming 11 (1) 117-127. 10.1007/BF01580380.

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Abstract

This paper addresses itself to the maximization of a convex quadratic function subject to linear constraints. We first prove the equivalence of this problem to the associated bilinear program. Next we apply the theory of bilinear programming developed in [9] to compute a local maximum and to generate a cutting plane which eliminates a region containing that local maximum. Then we develop an iterative procedure to improve a given cut by exploiting the symmetric structure of the bilinear program. This procedure either generates a point which is strictly better than the best local maximum found, or generates a cut which is deeper (usually much deeper) than Tui's cut. Finally the results of numerical experiments on small problems are reported.

Item Type:	Article
Research Programs:	System and Decision Sciences - Core (SDS)
Depositing User:	Romeo Molina
Date Deposited:	19 May 2016 07:03
Last Modified:	27 Aug 2021 17:27
URI:	https://pure.iiasa.ac.at/13229

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