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.
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© 1999 Springer Science+Business Media Dordrecht
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Floudas, C.A. et al. (1999). Twice Continuously Differentiable NLP Problems. In: Handbook of Test Problems in Local and Global Optimization. Nonconvex Optimization and Its Applications, vol 33. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3040-1_8
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DOI: https://doi.org/10.1007/978-1-4757-3040-1_8
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-4812-0
Online ISBN: 978-1-4757-3040-1
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