Applications to Continuous Problems

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

Abstract

In Part II of this book, we have presented a Reformulation-Linearization/Convexification Technique for generating tight polyhedral or convex relaxations for polynomial programming problems. We have shown how the lower bounds generated by this technique can be embedded within a branch-and-bound algorithm and used in concert with a suitable partitioning procedure in order to induce infinite convergence, in general, to a global optimum for the underlying nonconvex polynomial program. In some special cases, as we shall see in this chapter, finite convergence can be obtained by exploiting inherent problem structures and characteristics. In Chapters 8 and 9, we have also presented some particular RLT strategies for generating tight relaxations for quadratic as well as for general polynomial programs and have illustrated the computational strength of the proposed procedures. In the present chapter, we complement this discussion by describing the design of RLT-based algorithms for some other special applications. The purpose of this exposition is to exhibit by way of illustration how RLT can be used for constructing such algorithms for a variety of nonconvex programming problems.

Keywords

Linear Complementarity Problem Linear Programming Relaxation Basic Feasible Solution Supply Center Lagrangian Dual Problem 
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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