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Twice Continuously Differentiable NLP Problems

  • Christodoulos A. Floudas
  • Pãnos M. Pardalos
  • Claire S. Adjiman
  • William R. Esposito
  • Zeynep H. Gümüş
  • Stephen T. Harding
  • John L. Klepeis
  • Clifford A. Meyer
  • Carl A. Schweiger
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 33)

Abstract

Twice continuously differentiable NLPs represent a very broad class of problems with diverse applications in the fields of engineering, science, finance and economics. Specific problems include phase equilibrium characterization, minimum potential energy conformation of clusters and molecules, distillation sequencing, reactor network design, batch process design, VLSI chip design, protein folding, and portfolio optimization.

Keywords

Objective Function Test Problem Problem Statistics Global Solution Reactor Network 
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

  • Christodoulos A. Floudas
    • 1
  • Pãnos M. Pardalos
    • 2
  • Claire S. Adjiman
    • 1
  • William R. Esposito
    • 1
  • Zeynep H. Gümüş
    • 1
  • Stephen T. Harding
    • 1
  • John L. Klepeis
    • 1
  • Clifford A. Meyer
    • 1
  • Carl A. Schweiger
    • 1
  1. 1.Department of Chemical EngineeringPrinceton UniversityPrincetonUSA
  2. 2.Department of Industrial and Systems EngineeringUniversity of FloridaUSA

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