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Mixed Integer Nonlinear Programming

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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.
Request Code : ZLIBIO747990
Categories:
Year:
2012
Edition:
1
Publisher:
Springer-Verlag New York
Language:
English
Pages:
692
ISBN 10:
1461419263
ISBN 13:
9781461419273
ISBN:
1461419263,9781461419266,9781461419273
Series:
The IMA Volumes in Mathematics and its Applications 154
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