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A Probing Algorithm for MINLP with Failure Prediction by SVM

  • Giacomo Nannicini
  • Pietro Belotti
  • Jon Lee
  • Jeff Linderoth
  • François Margot
  • Andreas Wächter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6697)

Abstract

Bound tightening is an important component of algorithms for solving nonconvex Mixed Integer Nonlinear Programs. A probing algorithm is a bound-tightening procedure that explores the consequences of restricting a variable to a subinterval with the goal of tightening its bounds. We propose a variant of probing where exploration is based on iteratively applying a truncated Branch-and-Bound algorithm. As this approach is computationally expensive, we use a Support-Vector-Machine classifier to infer whether or not the probing algorithm should be used. Computational experiments demonstrate that the use of this classifier saves a substantial amount of CPU time at the cost of a marginally weaker bound tightening.

Keywords

Support Vector Machine Variable Bound Support Vector Machine Model Linear Programming Relaxation Failure Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Giacomo Nannicini
    • 1
  • Pietro Belotti
    • 2
  • Jon Lee
    • 3
  • Jeff Linderoth
    • 4
  • François Margot
    • 1
  • Andreas Wächter
    • 3
  1. 1.Tepper School of BusinessCarnegie Mellon UniversityPittsburgh
  2. 2.Dept. of Mathematical SciencesClemson UniversityClemson
  3. 3.IBM T. J. Watson Research CenterYorktown Heights
  4. 4.Industrial and Systems Eng.University of Wisconsin-MadisonMadison

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