Generating Valid Inequalities and Facets Using RLT

  • Hanif D. Sherali
  • Warren P. Adams
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 31)

Abstract

Thus far, we have presented a hierarchy of relaxations leading up to the convex hull representation for zero-one mixed-integer programming problems, and have developed extensions of this hierarchy to accommodate inherent special structures as well as to handle general discrete (as opposed to simply 0–1) variables. A key advantage of this development that we wish to discuss in the present chapter is that the RLT produces an algebraically explicit convex hull representation at the highest level. While it might not be computationally feasible to actually generate and solve the linear program based on this convex hull representation because of its potentially exponential size, there are other ways to exploit this information or facility to advantage as we shall presently see.

Keywords

Valid Inequality Polyhedral Cone Basic Feasible Solution Nonnegativity Constraint Extreme Direction 
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 Science+Business Media Dordrecht 1999

Authors and Affiliations

  • Hanif D. Sherali
    • 1
  • Warren P. Adams
    • 2
  1. 1.Department of Industrial and Systems EngineeringVirginia Polytechnic Institute and State UniversityBlacksburgUSA
  2. 2.Department of Mathematical SciencesClemson UniversityClemsonUSA

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