Twice Continuously Differentiable NLP Problems
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.
KeywordsObjective Function Test Problem Problem Statistics Global Solution Reactor Network
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