Pyomo -- optimization modeling in Python. 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.

References in zbMATH (referenced in 50 articles , 1 standard article )

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  1. Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres: PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance (2021) arXiv
  2. Bynum, Michael L.; Hackebeil, Gabriel A.; Hart, William E.; Laird, Carl D.; Nicholson, Bethany L.; Siirola, John D.; Watson, Jean-Paul; Woodruff, David L.: Pyomo -- optimization modeling in Python (2021)
  3. Francesco Ceccon, Ruth Misener: Solving the pooling problem at scale with extensible solver GALINI (2021) arXiv
  4. Maher, Stephen J.: Implementing the branch-and-cut approach for a general purpose Benders’ decomposition framework (2021)
  5. Bashier, Eihab B. M.: Practical numerical and scientific computing with MATLAB and Python (2020)
  6. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  7. Ceccon, Francesco; Siirola, John D.; Misener, Ruth: SUSPECT: MINLP special structure detector for Pyomo (2020)
  8. Christian D. Hubbs, Hector D. Perez, Owais Sarwar, Nikolaos V. Sahinidis, Ignacio E. Grossmann, John M. Wassick: OR-Gym: A Reinforcement Learning Library for Operations Research Problem (2020) arXiv
  9. Furman, Kevin C.; Sawaya, Nicolas W.; Grossmann, Ignacio E.: A computationally useful algebraic representation of nonlinear disjunctive convex sets using the perspective function (2020)
  10. Josiah Johnston; Rodrigo Henriquez-Auba; Benjamin Maluenda; Matthias Fripp: Switch 2.0: A modern platform for planning high-renewable power systems (2020) not zbMATH
  11. Kerr, Catherine; Hoare, Terri; Carroll, Paula; Mareček, Jakub: Integer programming ensemble of temporal relations classifiers (2020)
  12. Knueven, Bernard; Ostrowski, James; Watson, Jean-Paul: On mixed-integer programming formulations for the unit commitment problem (2020)
  13. Muts, Pavlo; Nowak, Ivo; Hendrix, Eligius M. T.: The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming (2020)
  14. Neumann, Christoph; Stein, Oliver; Sudermann-Merx, Nathan: Granularity in nonlinear mixed-integer optimization (2020)
  15. Singh, Bismark; Knueven, Bernard; Watson, Jean-Paul: Modeling flexible generator operating regions via chance-constrained stochastic unit commitment (2020)
  16. Stoyanova, Ivelina; Gümrükcü, Erdem; Aragon, Gustavo; Hidalgo-rodriguez, Diego I.; Monti, Antonello; Myrzik, Johanna: Distributed model predictive control strategies for coordination of electro-thermal devices in a cooperative energy management concept (2020)
  17. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  18. Cano-Belmán, Jaime; Meyr, Herbert: Deterministic allocation models for multi-period demand fulfillment in multi-stage customer hierarchies (2019)
  19. Júlvez, Jorge; Oliver, Stephen G.: Flexible nets: a modeling formalism for dynamic systems with uncertain parameters (2019)
  20. Júlvez, Jorge; Oliver, Stephen G.: Modeling, analyzing and controlling hybrid systems by guarded flexible nets (2019)

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