Coverart for item
The Resource Pyomo - Optimization Modeling in Python, by Michael L. Bynum, Gabriel A. Hackebeil, William E. Hart, Carl D. Laird, Bethany L. Nicholson, John D. Siirola, Jean-Paul Watson, David L. Woodruff, (electronic resource)

Pyomo - Optimization Modeling in Python, by Michael L. Bynum, Gabriel A. Hackebeil, William E. Hart, Carl D. Laird, Bethany L. Nicholson, John D. Siirola, Jean-Paul Watson, David L. Woodruff, (electronic resource)

Label
Pyomo - Optimization Modeling in Python
Title
Pyomo - Optimization Modeling in Python
Statement of responsibility
by Michael L. Bynum, Gabriel A. Hackebeil, William E. Hart, Carl D. Laird, Bethany L. Nicholson, John D. Siirola, Jean-Paul Watson, David L. Woodruff
Creator
Contributor
Author
Subject
Language
eng
Summary
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. Review of the Second edition: 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 .--
Member of
Is part of
Assigning source
Provided by publisher
http://library.link/vocab/creatorName
Bynum, Michael L
Image bit depth
0
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Hackebeil, Gabriel A
  • Hart, William E
  • Laird, Carl D
  • Nicholson, Bethany L
  • Siirola, John D
  • Watson, Jean-Paul
  • Woodruff, David L
Series statement
  • Springer Optimization and Its Applications,
  • Springer eBooks.
Series volume
67
http://library.link/vocab/subjectName
  • Mathematical optimization
  • Computer simulation
  • Computer mathematics
  • Computer science-Mathematics
  • Computer software
  • Operations research
  • Management science
Label
Pyomo - Optimization Modeling in Python, by Michael L. Bynum, Gabriel A. Hackebeil, William E. Hart, Carl D. Laird, Bethany L. Nicholson, John D. Siirola, Jean-Paul Watson, David L. Woodruff, (electronic resource)
Link
https://eui.idm.oclc.org/login?url=https://doi.org/10.1007/978-3-030-68928-5
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
1. Introduction -- Part I. An Introduction to Pyomo -- 2. Mathematical Modeling and Optimization -- 3. Pyomo Overview -- 4. Pyomo Models and Components: An Introduction -- 5. Scripting Custom Workflows -- 6. Interacting with Solvers -- Part II. Advanced Topics -- 7. Nonlinear Programming with Pyomo -- 8. Structured Modeling with Blocks -- 9. Performance: Model Construction and Solver Interfaces -- 10. Abstract Models and Their Solution -- Part III. Modeling Extensions -- 11. Generalized Disjunctive Programming -- 12. Differential Algebraic Equations -- 13. Mathematical Programs with Equilibrium Constraints -- . A Brief Python Tutorial -- Bibliography -- Index
Control code
978-3-030-68928-5
Dimensions
unknown
Edition
3rd ed. 2021.
Extent
1 online resource (XVII, 225 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Isbn
9783030689285
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
12 illustrations, 5 illustrations in color.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(OCoLC)1244620167
Label
Pyomo - Optimization Modeling in Python, by Michael L. Bynum, Gabriel A. Hackebeil, William E. Hart, Carl D. Laird, Bethany L. Nicholson, John D. Siirola, Jean-Paul Watson, David L. Woodruff, (electronic resource)
Link
https://eui.idm.oclc.org/login?url=https://doi.org/10.1007/978-3-030-68928-5
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
1. Introduction -- Part I. An Introduction to Pyomo -- 2. Mathematical Modeling and Optimization -- 3. Pyomo Overview -- 4. Pyomo Models and Components: An Introduction -- 5. Scripting Custom Workflows -- 6. Interacting with Solvers -- Part II. Advanced Topics -- 7. Nonlinear Programming with Pyomo -- 8. Structured Modeling with Blocks -- 9. Performance: Model Construction and Solver Interfaces -- 10. Abstract Models and Their Solution -- Part III. Modeling Extensions -- 11. Generalized Disjunctive Programming -- 12. Differential Algebraic Equations -- 13. Mathematical Programs with Equilibrium Constraints -- . A Brief Python Tutorial -- Bibliography -- Index
Control code
978-3-030-68928-5
Dimensions
unknown
Edition
3rd ed. 2021.
Extent
1 online resource (XVII, 225 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Isbn
9783030689285
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
12 illustrations, 5 illustrations in color.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(OCoLC)1244620167

Library Locations

    • Badia FiesolanaBorrow it
      Via dei Roccettini 9, San Domenico di Fiesole, 50014, IT
      43.803074 11.283055
Processing Feedback ...