Pyomo — Optimization Modeling in Python
Authors: Bynum, M.L., Hackebeil, G.A., Hart, W.E., Laird, C.D., Nicholson, B., Siirola, J.D., Watson, J.-P., Woodruff, D.L.
Free Preview- Third edition has been reoganized to provide better information flow for readers who are either new or experienced Pyomo users
- 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
- 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
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- About this Textbook
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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.
- About the authors
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William E. Hart, Carl D. Laird, Bethany L. Nicholson, John D. Siirola, and Michael L. Bynum are researchers affiliated with the Sandia National Laboratories in Albuquerque, New Mexico. Jean-Paul Watson is a researcher with the Lawrence Livermore Laboratory. David L. Woodruff is professor at the graduate school of management at the University of California, Davis. Gabriel Hackebeil is affiliated with Deepfield Nokia, Ann Arbor, MI. The 2019 INFORMS Computing Society prize was awarded to William E. Hart, Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil, Bethany L. Nicholson and John Siirola for spearheading the creation and advancement of Pyomo, an open-source software package for modeling and solving mathematical programs in Python.
- Table of contents (14 chapters)
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Introduction
Pages 1-11
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Mathematical Modeling and Optimization
Pages 15-24
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Pyomo Overview
Pages 25-36
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Pyomo Models and Components: An Introduction
Pages 37-65
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Scripting Custom Workflows
Pages 67-81
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Table of contents (14 chapters)
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- Pyomo — Optimization Modeling in Python
- Authors
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- Michael L. Bynum
- Gabriel A. Hackebeil
- William E. Hart
- Carl D. Laird
- Bethany Nicholson
- John D. Siirola
- Jean-Paul Watson
- David L. Woodruff
- Series Title
- Springer Optimization and Its Applications
- Series Volume
- 67
- Copyright
- 2021
- Publisher
- Springer International Publishing
- Copyright Holder
- This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply
- eBook ISBN
- 978-3-030-68928-5
- DOI
- 10.1007/978-3-030-68928-5
- Hardcover ISBN
- 978-3-030-68927-8
- Series ISSN
- 1931-6828
- Edition Number
- 3
- Number of Pages
- XVII, 225
- Number of Illustrations
- 7 b/w illustrations, 5 illustrations in colour
- Topics
