Pyomo

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 72 articles , 1 standard article )

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  1. Allen, Stephanie; Gabriel, Steven A.; Dickerson, John P.: Using inverse optimization to learn cost functions in generalized Nash games (2022)
  2. Beliakov, Gleb: Knapsack problems with dependencies through non-additive measures and Choquet integral (2022)
  3. Belli, Edoardo: Smoothly adaptively centered ridge estimator (2022)
  4. Chen, Qi; Johnson, Emma S.; Bernal, David E.; Valentin, Romeo; Kale, Sunjeev; Bates, Johnny; Siirola, John D.; Grossmann, Ignacio E.: Pyomo.GDP: an ecosystem for logic based modeling and optimization development (2022)
  5. Lundell, Andreas; Kronqvist, Jan: Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT (2022)
  6. Rossit, Diego G.; Nesmachnow, Segio; Toutouh, Jamal; Luna, Francisco: Scheduling deferrable electric appliances in smart homes: a bi-objective stochastic optimization approach (2022)
  7. Schewe, Lars; Schmidt, Martin; Thürauf, Johannes: Global optimization for the multilevel European gas market system with nonlinear flow models on trees (2022)
  8. Schmidt, Martin; Sirvent, Mathias; Wollner, Winnifried: The cost of not knowing enough: mixed-integer optimization with implicit Lipschitz nonlinearities (2022)
  9. Schweidtmann, Artur M.; Weber, Jana M.; Wende, Christian; Netze, Linus; Mitsos, Alexander: Obey validity limits of data-driven models through topological data analysis and one-class classification (2022)
  10. Singh, Bismark; Rehberg, Oliver; Groß, Theresa; Hoffmann, Maximilian; Kotzur, Leander; Stolten, Detlef: Budget-cut: introduction to a budget based cutting-plane algorithm for capacity expansion models (2022)
  11. Zhai, Jianyuan; Boukouvala, Fani: Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization (2022)
  12. Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres: PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance (2021) arXiv
  13. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Optimal randomized classification trees (2021)
  14. Bonvin, Gratien; Demassey, Sophie; Lodi, Andrea: Pump scheduling in drinking water distribution networks with an LP/NLP-based branch and bound (2021)
  15. 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)
  16. Fernández-Blanco, Ricardo; Morales, Juan Miguel; Pineda, Salvador; Porras, Álvaro: Inverse optimization with kernel regression: application to the power forecasting and bidding of a fleet of electric vehicles (2021)
  17. Francesco Ceccon, Ruth Misener: Solving the pooling problem at scale with extensible solver GALINI (2021) arXiv
  18. Kaut, Michal: Scenario generation by selection from historical data (2021)
  19. Li, Can; Bernal, David E.; Furman, Kevin C.; Duran, Marco A.; Grossmann, Ignacio E.: Sample average approximation for stochastic nonconvex mixed integer nonlinear programming via outer-approximation (2021)
  20. Mahajan, Ashutosh; Leyffer, Sven; Linderoth, Jeff; Luedtke, James; Munson, Todd: Minotaur: a mixed-integer nonlinear optimization toolkit (2021)

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