Mixed Integer Nonlinear Programming

Sven Leyffer

ISBN 10: 1493902210 / ISBN 13: 9781493902217

Published by Springer New York, Springer US, 2014

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9781493902217

Bibliographic Details

• Title: Mixed Integer Nonlinear Programming
• Publisher: Springer New York, Springer US
• Publication Date: 2014
• Language: English
• ISBN 10: 1493902210
• ISBN 13: 9781493902217
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 38D millimeters 235 millimeters width by 155 millimeters height by 38 millimeters depth
• Weight: 1060 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.