Pyomo - Optimization Modeling in Python

by William E. Hart, Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil

Overview

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.

Product Details

ISBN-13:	9783319864822
Publisher:	Springer International Publishing
Publication date:	08/03/2018
Series:	Springer Optimization and Its Applications , #67
Edition description:	Softcover reprint of the original 2nd ed. 2017
Pages:	277
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

William E. Hart, Jean-Paul Watson, Carl D. Laird, Bethany L. Nicholson, and John D. Siirola are researchers affiliated with the Sandia National Laboratories in Albuquerque, New Mexico. David Woodruff is professor is the graduate school of management at the University of California, Davis. Gabriel Hackebeil is a math programming consultant at the University of Michigan.

Table of Contents

1. Introduction.- Part I. An Introduction to Pyomo.- 2. Mathematical Modeling and Optimization.- 3. Pyomo Overview.- 4. Pyomo Models and Components.- 5. The Pyomo Command.- 6. Data Command Files.- Part II. Advanced Features and Extensions.- 7. Nonlinear Programming with Pyomo.- 8. Structured Modeling with Blocks.- 9. Generalized Disjunctive Programming.- 10. Shastic Programming Extensions.- 11. Differential Algebraic Equations.- 12. Mathematical Programs with Equilibrium Constraints.- 13. Bilevel Programming.- 14. Scripting.- A. A Brief Python Tutorial.- Index.

Editorial Reviews

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)

From the Publisher