Affiliations: Computer Science Department, The Pennsylvania State University, University Park, PA 16802, USA | Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA | Department of Management Sciences, The University of Iowa, Iowa City, Iowa 52242, USA
Note: [] Research supported in part by NSF Grant DDM-8922636 and the Iowa Business School Summer Grant.
Abstract: In this paper, we discuss computational aspects of an interior-point algorithm [1] for indefinite quadratic programming problems with box constraints. The algorithm finds a local minimizer by successively solving indefinite quadratic problems with an ellipsoid constraint. In addition, we present a sufficient condition for a local minimizer to be global, and we use this result to generate test problems with a known global solution. The proposed algorithm has been implemented on an IBM 3090 computer and tested on a variety of dense test problems, including problems with a known global optimizer.
Keywords: nonconvex quadratic programming, interior point algorithms, computational testing
DOI: 10.3233/INF-1992-3403
Journal: Informatica, vol. 3, no. 4, pp. 474-496, 1992
