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Journal of Global Optimization

, Volume 57, Issue 4, pp 1147–1172 | Cite as

Theoretical filtering of RLT bound-factor constraints for solving polynomial programming problems to global optimality

  • Evrim DalkiranEmail author
  • Hanif D. Sherali
Article

Abstract

In this paper, we propose two sets of theoretically filtered bound-factor constraints for constructing reformulation-linearization technique (RLT)-based linear programming (LP) relaxations for solving polynomial programming problems. We establish related theoretical results for convergence to a global optimum for these reduced sized relaxations, and provide insights into their relative sizes and tightness. Extensive computational results are provided to demonstrate the relative effectiveness of the proposed theoretical filtering strategies in comparison to the standard RLT and a prior heuristic filtering technique using problems from the literature as well as randomly generated test cases.

Keywords

Reformulation-linearization technique (RLT) Filtering strategies  Polynomial programming Branch-and-bound 

Notes

Acknowledgments

This research has been supported by the National Science Foundation under Grant No. CMMI-0969169. The authors also thank two anonymous referees for their constructive and insightful comments that have helped improve the substance and presentation in this paper

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Industrial and Systems EngineeringWayne State UniversityDetroitUSA
  2. 2.Grado Department of Industrial and Systems EngineeringVirginia TechBlacksburgUSA

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