Rights statement: This is the author’s version of a work that was accepted for publication in Operations Research Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Operations Research Letters, 46, 5, 2018 DOI: 10.1016/j.orl.2018.08.005
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A binarisation heuristic for non-convex quadratic programming with box constraints
AU - Galli, Laura
AU - Letchford, Adam Nicholas
N1 - This is the author’s version of a work that was accepted for publication in Operations Research Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Operations Research Letters, 46, 5, 2018 DOI: 10.1016/j.orl.2018.08.005
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Non-convex quadratic programming with box constraints is a fundamental problem in the global optimization literature, being one of the simplest NP-hard nonlinear programs. We present a new heuristic for this problem, which enables one to obtain solutions of excellent quality in reasonable computing times. The heuristic consists of four phases: binarisation, convexification, branch-and-Bound, and local optimisation. Some very encouraging computational results are given.
AB - Non-convex quadratic programming with box constraints is a fundamental problem in the global optimization literature, being one of the simplest NP-hard nonlinear programs. We present a new heuristic for this problem, which enables one to obtain solutions of excellent quality in reasonable computing times. The heuristic consists of four phases: binarisation, convexification, branch-and-Bound, and local optimisation. Some very encouraging computational results are given.
KW - global optimisation
KW - heuristics
KW - integer programming
U2 - 10.1016/j.orl.2018.08.005
DO - 10.1016/j.orl.2018.08.005
M3 - Journal article
VL - 46
SP - 529
EP - 533
JO - Operations Research Letters
JF - Operations Research Letters
SN - 0167-6377
IS - 5
ER -