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Feasibility-based bounds tightening via fixed points

  • Clemson University
  • University of Toulouse
  • IBM Watson Research Center

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

29 Citations (Scopus)

Abstract

The search tree size of the spatial Branch-and-Bound algorithm for Mixed-Integer Nonlinear Programming depends on many factors, one of which is the width of the variable ranges at every tree node. A range reduction technique often employed is called Feasibility Based Bounds Tightening, which is known to be practically fast, and is thus deployed at every node of the search tree. From time to time, however, this technique fails to converge to its limit point in finite time, thereby slowing the whole Branch-and-Bound search considerably. In this paper we propose a polynomial time method, based on solving a linear program, for computing the limit point of the Feasibility Based Bounds Tightening algorithm applied to linear equality and inequality constraints.

Original languageEnglish
Title of host publicationCombinatorial Optimization and Applications - 4th International Conference, COCOA 2010, Proceedings
PublisherSpringer Verlag
Pages65-76
Number of pages12
EditionPART 1
ISBN (Print)3642174574, 9783642174575
DOIs
Publication statusPublished - 1 Jan 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6508 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • MINLP
  • constraint programming
  • global optimization
  • range reduction
  • spatial Branch-and-Bound

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