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 41 articles , 1 standard article )

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  1. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  2. Ceccon, Francesco; Siirola, John D.; Misener, Ruth: SUSPECT: MINLP special structure detector for Pyomo (2020)
  3. 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
  4. Furman, Kevin C.; Sawaya, Nicolas W.; Grossmann, Ignacio E.: A computationally useful algebraic representation of nonlinear disjunctive convex sets using the perspective function (2020)
  5. Josiah Johnston; Rodrigo Henriquez-Auba; Benjamin Maluenda; Matthias Fripp: Switch 2.0: A modern platform for planning high-renewable power systems (2020) not zbMATH
  6. Muts, Pavlo; Nowak, Ivo; Hendrix, Eligius M. T.: The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming (2020)
  7. Neumann, Christoph; Stein, Oliver; Sudermann-Merx, Nathan: Granularity in nonlinear mixed-integer optimization (2020)
  8. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  9. Cano-Belmán, Jaime; Meyr, Herbert: Deterministic allocation models for multi-period demand fulfillment in multi-stage customer hierarchies (2019)
  10. Júlvez, Jorge; Oliver, Stephen G.: Modeling, analyzing and controlling hybrid systems by guarded flexible nets (2019)
  11. Júlvez, Jorge; Oliver, Stephen G.: Flexible nets: a modeling formalism for dynamic systems with uncertain parameters (2019)
  12. Nowak, Ivo; Muts, Pavlo; Hendrix, Eligius M. T.: Multi-tree decomposition methods for large-scale mixed integer nonlinear optimization (2019)
  13. Robinius, Martin; Schewe, Lars; Schmidt, Martin; Stolten, Detlef; Thürauf, Johannes; Welder, Lara: Robust optimal discrete arc sizing for tree-shaped potential networks (2019)
  14. Schenk, Christina: Book review of: W. E. Hart et al., Pyomo -- optimization modeling in Python. 2nd ed. (2019)
  15. Singham, D. I.: Sample average approximation for the continuous type principal-agent problem (2019)
  16. Valicka, Christopher G.; Garcia, Deanna; Staid, Andrea; Watson, Jean-Paul; Hackebeil, Gabriel; Rathinam, Sivakumar; Ntaimo, Lewis: Mixed-integer programming models for optimal constellation scheduling given cloud cover uncertainty (2019)
  17. Cai, W.; Singham, D. I.: A principal-agent problem with heterogeneous demand distributions for a carbon capture and storage system (2018)
  18. Costa, Alberto; Nannicini, Giacomo: RBFOpt: an open-source library for black-box optimization with costly function evaluations (2018)
  19. Jordan Jalving, Yankai Cao, Victor M. Zavala: Graph-Based Modeling and Simulation of Complex Systems (2018) arXiv
  20. Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul; Zavala, Victor M.; Biegler, Lorenz T.: \textttpyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations (2018)

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