Pyomo

<h2 id="features">Features</h2>
<p>Pyomo allows users to formulate <a href="/facts/Optimization_(mathematics)/oRn8Iv5I">optimization</a> problems in Python in a manner that is similar to the notation commonly used in mathematical optimization. Pyomo supports an object-oriented style of formulating optimization models, which are defined with a variety of modeling components:  sets, scalar and multidimensional parameters, decision variables, objectives, constraints, equations, disjunctions and more.  Optimization models can be initialized with python data, and external data sources can be defined using <a href="/facts/Spreadsheet/0cvc1GeB">spreadsheets</a>, <a href="/facts/Database/VYcpkevb">databases</a>, various formats of text files.  Pyomo supports both abstract models, which are defined without data, and concrete models, which are defined with data.  In both cases, Pyomo allows for the separation of model and data.
</p><p>Pyomo supports dozens of <a href="/facts/Solver/S8LurhlM">solvers</a>, both open source and commercial, including many solvers supported by <a href="/facts/AMPL/pzceW9mH">AMPL</a>, PICO, <a href="/facts/COIN-OR/we1fYPNj">CBC</a>, <a href="/facts/CPLEX/SnfwWLtb">CPLEX</a>, <a href="/facts/IPOPT/EdoDsb0Y">IPOPT</a>,  and <a href="/facts/GLPK/9j7hhN95">GLPK</a>. Pyomo can either invoke the solver directly or asynchronously with a solver manager.  Solver managers support remote, asynchronous execution of solvers, which supports parallel execution of Pyomo scripts.  Solver interaction is performed with a variety of solver interfaces, depending on the solver being used.  A very generic
solver interface is supported with AMPL's <a href="/facts/Nl_(format)/9U0EyckP">nl (format)</a>.
</p>
<h2 id="related-software">Related software</h2>
<p>The following software packages integrate Pyomo as a library to support optimization modeling and analysis:
</p>
<ul><li><a href="/facts/SolverStudio/h6mEFjDb">SolverStudio</a> lets you use Excel to edit, save and solve optimisation models built using a variety of modeling languages, including Pyomo.<a class="footnote-ref" id="fnref:3" href="#fn:3"><sup>3</sup></a> Pyomo is bundled with the <a href="http://solverstudio.org/">SolverStudio</a> software.</li>
<li><a href="http://www.temoaproject.org/">TEMOA</a> (Tools for Energy Model Optimization and Assessment) is an open source modeling framework for conducting energy system analysis.<a class="footnote-ref" id="fnref:4" href="#fn:4"><sup>4</sup></a> The core component of TEMOA is an energy economy optimization model. This model is formulated and optimized using Pyomo.</li>
<li><a href="https://adamgreenhall.github.io/minpower/">MinPower</a> is an open source toolkit for students and researchers in power systems. It is designed to make working with standard power system models simple and intuitive.<a class="footnote-ref" id="fnref:5" href="#fn:5"><sup>5</sup></a> MinPower uses Pyomo to formulate and optimize these power system models.</li>
<li><a href="https://github.com/PyPSA/linopy">linopy project</a>, offering similar functionality to Pyomo.<a class="footnote-ref" id="fnref:6" href="#fn:6"><sup>6</sup></a></li></ul>
<h2 id="see-also">See also</h2>
<ul><li><a href="/facts/Algebraic_modeling_language/0IXq6kFg">Algebraic modeling language</a></li></ul>

<h2 id="external-links">External links</h2>
<ul><li>Articles from IBM's developerWorks:
<ul><li>Gift, Noah (5 February 2013). <a href="https://developer.ibm.com/articles/cl-optimizepythoncloud1/">"Linear optimization in Python, Part 1: Solve complex problems in the cloud with Pyomo"</a>.</li>
<li><a href="http://www.ibm.com/developerworks/cloud/library/cl-optimizepythoncloud2/index.html">Linear optimization in Python, Part 2: Build a scalable architecture in the cloud</a></li></ul></li>
<li><a href="https://thedatabarista.wordpress.com/2015/01/27/pyomo-meets-fantasy-football/">"Pyomo Meets Fantasy Football"</a>. 2015-01-27.</li>
<li><a href="/facts/APOPT/uwFQSmAL">APOPT</a> Solver for <a href="https://github.com/APMonitor/apopt">LP, QP, MILP, NLP, and MINLP solutions in Pyomo</a></li></ul>

<h2 id="references">References</h2>

<ol>
<li id="fn:1"><p>William E. Hart; Carl D. Laird; Jean-Paul Watson; David L. Woodruff; Gabriel A. Hackebeil; Bethany L. Nicholson; John D. Siirola (2017). Pyomo — Optimization Modeling in Python. Springer. ISBN 978-3-319-58821-6. <a href="978-3-319-58821-6" target="_blank">978-3-319-58821-6</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li>
<li id="fn:2"><p>Hart, William; Jean-Paul Watson; David L. Woodruff (2011). "Pyomo: modeling and solving mathematical programs in python". Mathematical Programming Computation. Vol. 3, no. 3. doi:10.1007/s12532-011-0026-8. <a href="http://mpc.zib.de/index.php/mpc/article/viewfile/59/30" target="_blank">http://mpc.zib.de/index.php/mpc/article/viewfile/59/30</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li>
<li id="fn:3"><p>Mason, Andrew (2013). "SolverStudio: A New Tool for Better Optimisation and Simulation Modelling in Excel". INFORMS Transactions on Education. Vol. 14, no. 1. pp. 45–52. doi:10.1287/ited.2013.0112. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li>
<li id="fn:4"><p>DeCarolis, Joseph; Kevin Hunter; Sarat Sreepathi (2010). The TEMOA Project: Tools for Energy Model Optimization and Analysis (PDF). International Energy Workshop. Stockholm, Sweden. <a href="http://www.temoaproject.org/publications/DeCarolis_IEW2010_paper.pdf" target="_blank">http://www.temoaproject.org/publications/DeCarolis_IEW2010_paper.pdf</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li>
<li id="fn:5"><p>Greenhall, Adam; Rich Christie; Jean-Paul Watson (2012). Minpower: A power systems optimization toolkit (PDF). Power and Energy Society General Meeting. <a href="http://adamgreenhall.github.io/minpower/minpower.pdf" target="_blank">http://adamgreenhall.github.io/minpower/minpower.pdf</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li>
<li id="fn:6"><p>"linopy: Linear optimization with N-D labeled variables". PyPSA. Retrieved 2022-02-22. <a href="https://github.com/PyPSA/linopy" target="_blank">https://github.com/PyPSA/linopy</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li>
</ol>

Pyomo open-in-new

Pyomo