Mixed Integer Nonlinear Programming

Jon Lee editor Sven Leyffer editor

Format:Paperback

Publisher:Springer-Verlag New York Inc.

Published:3rd Mar '14

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Mixed Integer Nonlinear Programming cover

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.

ISBN: 9781493902217

Dimensions: unknown

Weight: 1074g

692 pages