A branch and bound algorithm for nonconvex quadratic optimization with ball and linear constraints

Amir Beck () and Dror Pan ()
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Amir Beck: Technion - Israel Institute of Technology
Dror Pan: Technion - Israel Institute of Technology

Journal of Global Optimization, 2017, vol. 69, issue 2, No 2, 309-342

Abstract: Abstract We suggest a branch and bound algorithm for solving continuous optimization problems where a (generally nonconvex) objective function is to be minimized under nonconvex inequality constraints which satisfy some specific solvability assumptions. The assumptions hold for some special cases of nonconvex quadratic optimization problems. We show how the algorithm can be applied to the problem of minimizing a nonconvex quadratic function under ball, out-of-ball and linear constraints. The main tool we utilize is the ability to solve in polynomial computation time the minimization of a general quadratic under one Euclidean sphere constraint, namely the so-called trust region subproblem, including the computation of all local minimizers of that problem. Application of the algorithm on sparse source localization problems is presented.

Keywords: Quadratically constrained quadratic problems; Nonconvex programming; Branch and bound; Sparse source localization; Trust region subproblem (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10898-017-0521-1

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