Maximization of a Convex Quadratic Function Under Linear Constraints

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/475 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/475 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/475 archive 351 disk0/00/00/04/56 2016-01-15 01:42:46 2021-08-27 17:08:09 2016-01-15 01:42:46 monograph show 4 Konno H. AL0204 Maximization of a Convex Quadratic Function Under Linear Constraints pub iis_met prog_sds This paper addresses 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 a theory of bilinear programming 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. 1975-11 published RM-75-060 RM-75-060 10 2058 public research_memorandum IIASA, Laxenburg, Austria 18 FALSE info:eu-repo/semantics/book