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Reformulation-Convexification Technique for Polynomial Programs: Design and Implementation

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

Abstract

In Chapter 7, we discussed the design and theory of a Reformulation-Linearization Technique for solving polynomial programming problems. We now return to this class of problems, but this time, we shall focus on specific implementation issues and describe various algorithmic strategies and additional classes of RLT constraints that can be gainfully employed in enhancing the performance of an RLT-based solution procedure. For the sake of convenience, we restate below the type of polynomial programming problems considered here, as introduced in Chapter 7.

Keywords

Linear Programming Relaxation Algorithmic Strategy Quadratic Inequality Multivariate Problem Quadratic Equality 
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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