Mixed Integer Nonlinear Programming - Info and Reading Options
By Jon . Lee and Sven Leyffer
"Mixed Integer Nonlinear Programming" was published by Springer in 2012 - New York, the book is classified in Mathematics genre, it has 690 pages and the language of the book is English.
“Mixed Integer Nonlinear Programming” Metadata:
- Title: ➤ Mixed Integer Nonlinear Programming
- Authors: Jon . LeeSven Leyffer
- Language: English
- Number of Pages: 690
- Is Family Friendly: Yes - No Mature Content
- Publisher: Springer
- Publish Date: 2012
- Publish Location: New York
- Genres: Mathematics
“Mixed Integer Nonlinear Programming” Subjects and Themes:
- Subjects: ➤ Mathematical optimization - Nonlinear programming - Integer programming - Algorithms - Mathematics - Approximations and Expansions - Continuous Optimization
Edition Specifications:
- Pagination: xvii, 690 p. :
Edition Identifiers:
- Google Books ID: x6wEkgEACAAJ
- The Open Library ID: OL25223099M - OL16531518W
- Online Computer Library Center (OCLC) ID: 766331116
- Library of Congress Control Number (LCCN): 2011942482
- ISBN-13: 9781461419266
- ISBN-10: 1461419263
- All ISBNs: 1461419263 - 9781461419266
AI-generated Review of “Mixed Integer Nonlinear Programming”:
Snippets and Summary:
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.
"Mixed Integer Nonlinear Programming" Description:
Google Books:
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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