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- W2053236872 abstract "A global optimization algorithm for nonconvex Generalized Disjunctive Programming (GDP) problems is proposed in this paper. By making use of convex underestimating functions for bilinear, linear fractional and concave separable functions in the continuous variables, the convex hull of each nonlinear disjunction is constructed. The relaxed convex GDP problem is then solved in the first level of a two-level branch and bound algorithm, in which a discrete branch and bound search is performed on the disjunctions to predict lower bounds. In the second level, a spatial branch and bound method is used to solve nonconvex NLP problems for updating the upper bound. The proposed algorithm exploits the convex hull relaxation for the discrete search, and the fact that the spatial branch and bound is restricted to fixed discrete variables in order to predict tight lower bounds. Application of the proposed algorithm to several example problems is shown, as well as a comparison with other algorithms." @default.
- W2053236872 created "2016-06-24" @default.
- W2053236872 creator A5056017028 @default.
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- W2053236872 date "2001-11-01" @default.
- W2053236872 modified "2023-10-18" @default.
- W2053236872 title "A global optimization algorithm for nonconvex generalized disjunctive programming and applications to process systems" @default.
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- W2053236872 doi "https://doi.org/10.1016/s0098-1354(01)00732-3" @default.
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