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Pyomo - Optimization Modeling in Python (Springer Optimization and Its Applications, Vol. 67) Hardcover – February 9, 2012

ISBN-13: 978-1461432258 ISBN-10: 1461432251 Edition: 1st

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Product Details

  • Hardcover: 238 pages
  • Publisher: Springer; 1st edition (February 9, 2012)
  • Language: English
  • ISBN-10: 1461432251
  • ISBN-13: 978-1461432258
  • Product Dimensions: 6.1 x 0.6 x 9.2 inches
  • Shipping Weight: 1.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Best Sellers Rank: #1,972,345 in Books (See Top 100 in Books)

Editorial Reviews

Review

Documents a simple, yet versatile tool for modeling and solving optimization problems. ... The book, by Bill Hart, Carl Laird, 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

From the Back Cover

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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Most Helpful Customer Reviews

1 of 1 people found the following review helpful By Cesar on April 15, 2014
Format: Hardcover Verified Purchase
I've been using promo for the last 2 years and this book goes in detail over most of the fundamentals of how to use Pyomo. It gives plenty of coding examples and it goes over scripting principles, which is what makes Pyomo a very versatile tool compared to other optimization languages such as AMPL.
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Format: Hardcover
I purchased this directly from Springer, where it is cheaper and more usable due to pdf format!
The book deals in-depth with Pyomo and partly COOPR for general optimization modeling problems.
The online Pyomo manual freely available on Sandia website does not provide all the details necessary to handle advanced use cases and refers to this book sometimes. So use combination of free manual and this book to get best experience.
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1 of 2 people found the following review helpful By Antonello Lobianco on July 31, 2012
Format: Hardcover
My background: I'm a economic modeller and amateur programmer, pretty used with optimisation software.
The problem of this book is that the software that it covers requires a not-trivial installation, at least compared with "competing products" like GAMS or AMPL: without going too much into details, Pyopt requires the Python programming language, the Coopr stack of software and, not less important, the individual solvers.
The book doesn't try to give a clear and comprehensive picture on how to use the described software. Rather, it starts describing the software own language, relegating the how-to-use instructions on small points spread trough the book.
For example it tells how to install Coopr in 5 different ways, but it doesn't tell where to find the pyopt executable, or where to download/find the code of the examples described in the book.
As a further drawback the examples, on my opinion, are not so clear and too linked with specific domains.
That's a pity because the underlying idea of a library that extend a general-purpose language for the specific needs of mathematical programming (rather than using a specific language) is very interesting for all that models that deal with optimisation but also do other.
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