Tightening McCormick Relaxations for Nonlinear Programs via Dynamic Multivariate Partitioning

  • Harsha Nagarajan
  • Mowen Lu
  • Emre Yamangil
  • Russell Bent
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9892)

Abstract

In this work, we propose a two-stage approach to strengthen piecewise McCormick relaxations for mixed-integer nonlinear programs (MINLP) with multi-linear terms. In the first stage, we exploit Constraint Programing (CP) techniques to contract the variable bounds. In the second stage we partition the variables domains using a dynamic multivariate partitioning scheme. Instead of equally partitioning the domains of variables appearing in multi-linear terms, we construct sparser partitions yet tighter relaxations by iteratively partitioning the variable domains in regions of interest. This approach decouples the number of partitions from the size of the variable domains, leads to a significant reduction in computation time, and limits the number of binary variables that are introduced by the partitioning. We demonstrate the performance of our algorithm on well-known benchmark problems from MINLPLIB and discuss the computational benefits of CP-based bound tightening procedures.

Keywords

McCormick relaxations MINLP Dynamic partitioning Bound tightening 

Notes

Acknowledgements

The work was funded by the Center for Nonlinear Studies (CNLS) and was carried out under the auspices of the NNSA of the U.S. DOE at LANL under Contract No. DE-AC52-06NA25396.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Harsha Nagarajan
    • 1
  • Mowen Lu
    • 2
  • Emre Yamangil
    • 1
  • Russell Bent
    • 1
  1. 1.Center for Nonlinear StudiesLos Alamos National LaboratoryLos AlamosUSA
  2. 2.Department of Industrial EngineeringClemson UniversityClemsonUSA

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