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Mixed Integer Nonlinear Programming

Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization; but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners ¿ including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers ¿ are interested in solving large-scale MINLP instances.
EAN: 9781461419266
Auflage: 2012
Sprache: Englisch
Seitenzahl: 712
Produktart: Gebunden
Herausgeber: Leyffer, Sven Lee, Jon
Verlag: Springer New York Springer US, New York, N.Y.
Veröffentlichungsdatum: 01.12.2011
Schlagworte: Algorithmus Approximation - Differenzenapproximation Näherungsrechnung Rechnen / Näherungsrechnung
Größe: 43 × 160 × 241
Gewicht: 1221 g

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Mixed Integer Nonlinear Programming
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