Towards the Implementation of Successive Convex Relaxation
Method for Nonconvex Quadratic Optimization Problems
Akiko Takeda, Yang Dai, Mituhiro Fukuda, and Masakazu Kojima
Recently Kojima and Tun\c{c}el proposed new successive
convex relaxation methods and their localized-discretized
variants for general nonconvex quadratic programs.
Although an upper bound of the objective function value
within {\sl a prior} precision can be found theoretically by solving a finite
number of linear programs, several important implementation problems remain
unsolved. In this paper we discuss these issues, present practically
implementable algorithms and report numerical results.
Research Reports on Information Sciences, No. B-347,
Department of Mathematical and Computing Sciences,
Tokyo Institute of Technology, March 1999.
Contact: [email protected]
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