ENGLISH

Mixed Integer Nonlinear Programming

By Jon Lee , Jeff Linderoth (auth.) , Pierre Bonami , Mustafa Kilinç , Sven Leyffer (eds.)

Book information

Publisher
Springer-Verlag New York
Year
2012
ISBN
9781461419266, 1461419263
DOI
10.1007/978-1-4614-1927-3
Language
english
Format
PDF
Filesize
5 MB (4943098 bytes)
Series
The IMA Volumes in Mathematics and its Applications 154
Edition
1
Pages
692\711
Library
Kolxo3
Time added
2012-08-21 20:00:00
Approximations and Expansions; Algorithms; Continu

Description

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.

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