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 65 articles , 1 standard article )

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  1. Belli, Edoardo: Smoothly adaptively centered ridge estimator (2022)
  2. Lundell, Andreas; Kronqvist, Jan: Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT (2022)
  3. Schewe, Lars; Schmidt, Martin; Thürauf, Johannes: Global optimization for the multilevel European gas market system with nonlinear flow models on trees (2022)
  4. Zhai, Jianyuan; Boukouvala, Fani: Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization (2022)
  5. Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres: PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance (2021) arXiv
  6. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Optimal randomized classification trees (2021)
  7. Bonvin, Gratien; Demassey, Sophie; Lodi, Andrea: Pump scheduling in drinking water distribution networks with an LP/NLP-based branch and bound (2021)
  8. 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)
  9. 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)
  10. Francesco Ceccon, Ruth Misener: Solving the pooling problem at scale with extensible solver GALINI (2021) arXiv
  11. Kaut, Michal: Scenario generation by selection from historical data (2021)
  12. 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)
  13. Mahajan, Ashutosh; Leyffer, Sven; Linderoth, Jeff; Luedtke, James; Munson, Todd: Minotaur: a mixed-integer nonlinear optimization toolkit (2021)
  14. Maher, Stephen J.: Implementing the branch-and-cut approach for a general purpose Benders’ decomposition framework (2021)
  15. Muts, Pavlo; Nowak, Ivo; Hendrix, Eligius M. T.: On decomposition and multiobjective-based column and disjunctive cut generation for MINLP (2021)
  16. Neumann, Christoph; Stein, Oliver: Generating feasible points for mixed-integer convex optimization problems by inner parallel cuts (2021)
  17. Sheng Dai, Yu-Hsueh Fang, Chia-Yen Lee, Timo Kuosmanen: pyStoNED: A Python Package for Convex Regression and Frontier Estimation (2021) arXiv
  18. Tanneau, Mathieu; Anjos, Miguel F.; Lodi, Andrea: Design and implementation of a modular interior-point solver for linear optimization (2021)
  19. Ushijima-Mwesigwa, Hayato; Khan, MD Zadid; Chowdhury, Mashrur A.; Safro, Ilya: Optimal placement of wireless charging lanes in road networks (2021)
  20. Bashier, Eihab B. M.: Practical numerical and scientific computing with MATLAB and Python (2020)

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