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Branch and Bound for Global NLP: New Bounding LP

  • Thomas G. W. Epperly
  • Ross E. Swaney
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 9)

Abstract

We present here a new method for bounding nonlinear programs which forms the foundation for a branch and bound algorithm presented in the next chapter. The bounding method is a generalization of the method proposed by Swaney [34] and is applicable to NLPs in factorable form, which include problems with quadratic objective functions and quadratic constraints as well as problems with twice differentiable transcendental functions. This class of problems is wide enough to cover many useful engineering applications including the following which have appeared in the literature: phase and chemical equilibrium problems [5, 15, 16], complex reactor networks [5], heat exchanger networks [5, 23, 38], pool blending [36, 37], and flowsheet optimization [5, 28]. Reviews of the applications of general nonlinear and bilinear programs are available in [1, 5].

Keywords

Null Space Feasible Point Global Optimization Algorithm Newton Step Convex Envelope 
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 1996

Authors and Affiliations

  • Thomas G. W. Epperly
    • 1
  • Ross E. Swaney
    • 1
  1. 1.Department of Chemical EngineeringUniversity of WisconsinMadisonUSA

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