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A Probing Algorithm for MINLP with Failure Prediction by SVM

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Part of the Lecture Notes in Computer Science book series (LNTCS,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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Nannicini, G., Belotti, P., Lee, J., Linderoth, J., Margot, F., Wächter, A. (2011). A Probing Algorithm for MINLP with Failure Prediction by SVM. In: Achterberg, T., Beck, J.C. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2011. Lecture Notes in Computer Science, vol 6697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21311-3_15

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  • DOI: https://doi.org/10.1007/978-3-642-21311-3_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21310-6

  • Online ISBN: 978-3-642-21311-3

  • eBook Packages: Computer ScienceComputer Science (R0)