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. DOI:10.1007/BF01580380.

Full text not available from this repository.

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: 19 May 2016 07:03
URI: http://pure.iiasa.ac.at/13229

Actions (login required)

View Item View Item

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313