This is a comprehensive guide to Pyomo, an open source software package for formulating and solving large-scale optimization and operations research problems. Includes chapters on advanced modeling capabilities for nonlinear and stochastic optimization.

This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. The text illustrates the breadth of the modeling and analysis capabilities that are supported by the software and support of complex real-world applications. Pyomo is an open source software package for formulating and solving large-scale optimization and operations research problems. The text begins with a tutorial on simple linear and integer programming models. A detailed reference of Pyomo's modeling components is illustrated with extensive examples, including a discussion of how to load data from data sources like spreadsheets and databases. Chapters describing advanced modeling capabilities for nonlinear and stochastic optimization are also included. The Pyomo software provides familiar modeling features within Python, a powerful 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. The software supports a different modeling approach than commercial AML (Algebraic Modeling Languages) tools, and is designed for flexibility, extensibility, portability, and maintainability but also maintains the central ideas in modern AMLs.

Unique book describing the 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? Includes supplementary material: sn.pub/extras

Autorentext

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.

Klappentext

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. Modeling is a fundamental process in many aspects of scientific research, engineering, and business. This text beautifully illustrates the breadth of the modeling capabilities that are supported by this new software and its handling of complex real-world applications.

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.

The text begins with a tutorial on simple linear and integer programming models. Information needed to install and get started with the software is also provided. A detailed reference of Pyomo's modeling components is illustrated with extensive examples, including a discussion of how to load data from sources like spreadsheets and databases. The final chapters cover advanced topics such as nonlinear models, stochastic models, and scripting examples.

Inhalt
Preface.- 1. Introduction.- 2. Pyomo Modeling Strategies.- 3. Model Components: Variables, Objectives and Constraints.- 4. Model Components: Sets and Parameters.- 5. Mischellaneous Model Components and Utility Functions.- 6. Initializing Abstract Models with Data Command Files.- 7. The Pyomo Command-Line Interface.- 8. Nonlinear Programming with Pyomo.- 9. Stochastic Programming Extensions.- 10. Scripting and Algorithm Development.- A. Installing Coopr.- B. A Brief Python Tutorial.- C. Pyomo and Coopr: The Bigger Picture.- Index.