Mixed Integer Nonlinear Programming

Sven Leyffer

ISBN 10: 1461419263 / ISBN 13: 9781461419266

Published by Springer New York, Springer New York Dez 2011, 2011

About this Item

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 712 pp. Englisch.

Seller Inventory # 9781461419266

Bibliographic Details

• Title: Mixed Integer Nonlinear Programming
• Publisher: Springer New York, Springer New York Dez 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 1461419263
• ISBN 13: 9781461419266
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 43D millimeters 241 millimeters width by 160 millimeters height by 43 millimeters depth
• Weight: 1221 grams

About this title

Synopsis

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.