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 33 articles , 1 standard article )

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  1. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  2. Blackburn, Landen; Young, Aaron; Rogers, Pratt; Hedengren, John; Powell, Kody: Dynamic optimization of a district energy system with storage using a novel mixed-integer quadratic programming algorithm (2019)
  3. Cano-Belmán, Jaime; Meyr, Herbert: Deterministic allocation models for multi-period demand fulfillment in multi-stage customer hierarchies (2019)
  4. Júlvez, Jorge; Oliver, Stephen G.: Modeling, analyzing and controlling hybrid systems by guarded flexible nets (2019)
  5. Kronqvist, Jan; Bernal, David E.; Lundell, Andreas; Grossmann, Ignacio E.: A review and comparison of solvers for convex MINLP (2019)
  6. Robinius, Martin; Schewe, Lars; Schmidt, Martin; Stolten, Detlef; Thürauf, Johannes; Welder, Lara: Robust optimal discrete arc sizing for tree-shaped potential networks (2019)
  7. Schenk, Christina: Book review of: W. E. Hart et al., Pyomo -- optimization modeling in Python. 2nd ed. (2019)
  8. Singham, D. I.: Sample average approximation for the continuous type principal-agent problem (2019)
  9. 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)
  10. Cai, W.; Singham, D. I.: A principal-agent problem with heterogeneous demand distributions for a carbon capture and storage system (2018)
  11. Costa, Alberto; Nannicini, Giacomo: RBFOpt: an open-source library for black-box optimization with costly function evaluations (2018)
  12. Jordan Jalving, Yankai Cao, Victor M. Zavala: Graph-Based Modeling and Simulation of Complex Systems (2018) arXiv
  13. 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)
  14. Nowak, Ivo; Breitfeld, Norman; Hendrix, Eligius M. T.; Njacheun-Njanzoua, Grégoire: Decomposition-based inner- and outer-refinement algorithms for global optimization (2018)
  15. Barnett, Jason; Watson, Jean-Paul; Woodruff, David L.: BBPH: using progressive hedging within branch and bound to solve multi-stage stochastic mixed integer programs (2017)
  16. Chen, Hung-Hsin; Yang, Chang-Biau: Multiperiod portfolio investment using stochastic programming with conditional value at risk (2017)
  17. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  18. Gemine, Quentin; Ernst, Damien; Cornélusse, Bertrand: Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution (2017)
  19. Hart, William E.; Laird, Carl D.; Watson, Jean-Paul; Woodruff, David L.; Hackebeil, Gabriel A.; Nicholson, Bethany L.; Siirola, John D.: Pyomo -- optimization modeling in Python (2017)
  20. Kersting, Kristian; Mladenov, Martin; Tokmakov, Pavel: Relational linear programming (2017)

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