William E. Hart Carl D. Laird Jean-Paul Watson David L. Woodruff Gabriel A. Hackebeil Bethany L. Nicholson John D. Siirola Hart Pyomo — Optimization Modeling in Python

Pyomo — Optimization Modeling in Python

von William E. Hart Carl D. Laird Jean-Paul Watson David L. Woodruff Gabriel A. Hackebeil Bethany L. Nicholson John D. Siirola

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Beschreibung

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. This second edition provides an expanded presentation of Pyomo’s modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming.

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 first edition:

Documents a simple, yet versatile tool for modeling and solving optimization problems. … The book, by Bill Hart, CarlLaird, Jean-Paul Watson, and David Woodruff, is essential to the usability of Pyomo, serving as the Pyomo documentation. … has contents for both an inexperienced user, and a computational operations research expert. … with examples of each of the concepts discussed.

 —Nedialko B. Dimitrov, INFORMS Journal on Computing, Vol. 24 (4), Fall 2012


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. This second edition provides an expanded presentation of Pyomo’s modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming.

Pyomois 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.


Unique book describing the user-friendly Pyomo modeling tool, the most comprehensive open source modeling software that can model linear programs, integer programs, nonlinear programs, stochastic programs and disjunctive programs Second edition present additional PYOMO capabilities not appearing in other sources Discusses Pyomo's modeling components, illustrated with extensive examples Introduces beginners to the software and presents chapters for advanced modeling capabilities? Contains a comprehensive tutorial Includes supplementary material: sn.pub/extras

Autor*in

William E. Hart

Themen in »Pyomo — Optimization Modeling in Python«

Pyomo modeling library Python optimization modeling and simulation mathematical modeling tool algebraic modeling languages Python script Python data hybrid optimization Matplotlib NumPy SciPy PySP Pyomo tutorial

Stimmen zu »Pyomo — Optimization Modeling in Python«

“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)
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Details

ISBN: 9783319864822
Verlag: Springer International Publishing
Erscheinung: 04.08.2018

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