Relaxations of Factorable Programs

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


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).


Convex Function Supporting Line Symmetric Difference Projective Distance Algorithm Relax 
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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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