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Global Optimization in Engineering Design pp 1–35Cite as

Branch and Bound for Global NLP: New Bounding LP

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Part of the book series: Nonconvex Optimization and Its Applications ((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].

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Author information

Authors and Affiliations

  1. Department of Chemical Engineering, University of Wisconsin, Madison, Wisconsin, USA

    Thomas G. W. Epperly & Ross E. Swaney

Authors
  1. Thomas G. W. Epperly
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  2. Ross E. Swaney
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Editor information

Editors and Affiliations

  1. Carnegie Mellon University, USA

    Ignacio E. Grossmann

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© 1996 Springer Science+Business Media Dordrecht

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Epperly, T.G.W., Swaney, R.E. (1996). Branch and Bound for Global NLP: New Bounding LP. In: Grossmann, I.E. (eds) Global Optimization in Engineering Design. Nonconvex Optimization and Its Applications, vol 9. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5331-8_1

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  • DOI: https://doi.org/10.1007/978-1-4757-5331-8_1

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4754-3

  • Online ISBN: 978-1-4757-5331-8

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