Enhancing the normalized multiparametric disaggregation technique for mixed-integer quadratic programming

Tiago Andrade (), Fabricio Oliveira (), Silvio Hamacher () and Andrew Eberhard ()
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Tiago Andrade: Pontifícia Universidade Católica do Rio de Janeiro (Puc-Rio) R. Marquês de São Vicente
Fabricio Oliveira: Aalto University
Silvio Hamacher: Pontifícia Universidade Católica do Rio de Janeiro (Puc-Rio) R. Marquês de São Vicente
Andrew Eberhard: RMIT University

Journal of Global Optimization, 2019, vol. 73, issue 4, No 2, 722 pages

Abstract: Abstract We propose methods for improving the relaxations obtained by the normalized multiparametric disaggregation technique (NMDT). These relaxations constitute a key component for some methods for solving nonconvex mixed-integer quadratically constrained quadratic programming (MIQCQP) problems. It is shown that these relaxations can be more efficiently formulated by significantly reducing the number of auxiliary variables (in particular, binary variables) and constraints. Moreover, a novel algorithm for solving MIQCQP problems is proposed. It can be applied using either its original NMDT or the proposed reformulation. Computational experiments are performed using both benchmark instances from the literature and randomly generated instances. The numerical results suggest that the proposed techniques can improve the quality of the relaxations.

Keywords: Normalized multiparametric disaggregation technique; Nonconvex mixed-integer quadratically constrained quadratic programs; McCormick envelopes; Convex relaxation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10898-018-0728-9

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