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Relaxations of Factorable Programs

  • Mohit Tawarmalani
  • Nikolaos V. Sahinidis
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 65)

Synopsis

In Chapter 2, we developed a theory of convex extensions and formalized a technique for building tight relaxations. However, the size of the resulting relaxations can be exponential in the number of variables. In this chapter, we present a slightly modified version of the factorable programming technique due to McCormick (1976) that, when used in conjunction with our relaxation techniques, constructs relaxations that are tight as well as manageable in size and can be generated in an automated fashion. To enable the use of efficient LP software, in Section 4.2 we build linear programming relaxations of the nonlinear convex relaxations using the sandwich algorithm (Rote 1992).

Keywords

Convex Function Supporting Line Symmetric Difference Projective Distance Algorithm Relax 
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 2002

Authors and Affiliations

  • Mohit Tawarmalani
    • 1
  • Nikolaos V. Sahinidis
    • 2
  1. 1.Purdue UniversityWest LafayetteUSA
  2. 2.University of IllinoisUrbanaUSA

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