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A Binarisation Approach to Non-Convex Quadratically Constrained Quadratic Programs

Laura Galli(laura.galli***at***unipi.it)
Adam N Letchford(A.N.Letchford***at***lancaster.ac.uk)

Abstract: Global optimisation of non-convex quadratically constrained quadratic programs is a notoriously difficult problem, being not only NP-hard in the strong sense, but also very difficult in practice. We present a new heuristic approach to this problem, which enables one to obtain solutions of good quality in reasonable computing times. The heuristic consists of four phases: binarisation, convexification, branch-and-bound and local optimisation. Computational results, on box-constrained and point packing instances, are encouraging.

Keywords: quadratically constrained quadratic programming; mixed-integer nonlinear programming; heuristics

Category 1: Global Optimization

Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 3: Nonlinear Optimization (Quadratic Programming )

Citation: Working paper, Department of Management Science, Lancaster University, February 2015.

Download: [PDF]

Entry Submitted: 05/20/2015
Entry Accepted: 05/20/2015
Entry Last Modified: 05/20/2015

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