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

Tiago Andrade, Fabricio Oliveira, Silvio Hamacher, Andrew Eberhard*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
43 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)701–722
JournalJOURNAL OF GLOBAL OPTIMIZATION
Volume73
Issue number4
Early online date17 Dec 2018
DOIs
Publication statusPublished - Apr 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • Convex relaxation
  • McCormick envelopes
  • Nonconvex mixed-integer quadratically constrained quadratic programs
  • Normalized multiparametric disaggregation technique

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