Ebook: Pyomo – Optimization Modeling in Python

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.

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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. 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.

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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. 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.

Content:
Front Matter....Pages i-xviii
Introduction....Pages 1-11
Pyomo Modeling Strategies....Pages 13-27
Model Components: Variables, Objectives, and Constraints....Pages 29-41
Model Components: Sets and Parameters....Pages 43-55
Miscellaneous Model Components and Utility Functions....Pages 57-65
Initializing Abstract Models with Data Command Files....Pages 67-89
The Pyomo Command-line Interface....Pages 91-103
Nonlinear Programming with Pyomo....Pages 105-129
Stochastic Programming Extensions....Pages 131-164
Scripting and Algorithm Development....Pages 165-203
Back Matter....Pages 205-237

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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. 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.

Content:
Front Matter....Pages i-xviii
Introduction....Pages 1-11
Pyomo Modeling Strategies....Pages 13-27
Model Components: Variables, Objectives, and Constraints....Pages 29-41
Model Components: Sets and Parameters....Pages 43-55
Miscellaneous Model Components and Utility Functions....Pages 57-65
Initializing Abstract Models with Data Command Files....Pages 67-89
The Pyomo Command-line Interface....Pages 91-103
Nonlinear Programming with Pyomo....Pages 105-129
Stochastic Programming Extensions....Pages 131-164
Scripting and Algorithm Development....Pages 165-203
Back Matter....Pages 205-237
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