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＜電子ブック＞
Pyomo — Optimization Modeling in Python / by Michael L. Bynum, Gabriel A. Hackebeil, William E. Hart, Carl D. Laird, Bethany L. Nicholson, John D. Siirola, Jean-Paul Watson, David L. Woodruff
(Springer Optimization and Its Applications. ISSN:19316836 ; 67)

版	3rd ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	XVII, 225 p. 12 illus., 5 illus. in color : online resource
著者標目	*Bynum, Michael L author Hackebeil, Gabriel A author Hart, William E author Laird, Carl D author Nicholson, Bethany L author Siirola, John D author Watson, Jean-Paul author Woodruff, David L author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Computer simulation LCSH:Mathematics -- Data processing  全ての件名で検索 LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Computer software LCSH:Operations research LCSH:Management science FREE:Optimization FREE:Computer Modelling FREE:Computational Mathematics and Numerical Analysis FREE:Mathematical Applications in Computer Science FREE:Mathematical Software FREE:Operations Research, Management Science
一般注記	1. Introduction -- Part I. An Introduction to Pyomo -- 2. Mathematical Modeling and Optimization -- 3. Pyomo Overview -- 4. Pyomo Models and Components: An Introduction -- 5. Scripting Custom Workflows -- 6. Interacting with Solvers -- Part II. Advanced Topics -- 7. Nonlinear Programming with Pyomo -- 8. Structured Modeling with Blocks -- 9. Performance: Model Construction and Solver Interfaces -- 10. Abstract Models and Their Solution -- Part III. Modeling Extensions -- 11. Generalized Disjunctive Programming -- 12. Differential Algebraic Equations -- 13. Mathematical Programs with Equilibrium Constraints -- . A Brief Python Tutorial -- Bibliography -- Index This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. In the third edition, much of the material has been reorganized, new examples have been added, and a new chapter has been added describing how modelers can improve the performance of their models. The authors have also modified their recommended method for importing Pyomo. A big change in this edition is the emphasis of concrete models, which provide fewer restrictions on the specification and use of Pyomo models. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. Review of the Second edition: This book provides a detailed guide to Pyomo for beginners and advanced users from undergraduate students to academic researchers to practitioners. … the book is a good software guide which I strongly recommend to anybody interested in looking for an alternative to commercial modeling languages in general or in learning or intensifying their Pyomo skills in particular. —Christina Schenk, SIAM Review, Vol. 61 (1), March 2019 HTTP:URL=https://doi.org/10.1007/978-3-030-68928-5

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