Pyomo

To help you get started, we've collected the most common ways that Pyomo is being used within popular public projects.

cog-imperial / suspect / tests / polynomial / test_rules.py View on Github

def test_division_rule_with_nonconstant_denominator(visitor, den_degree):
    assume(not den_degree.is_constant())
    child = pe.Var()
    num = 1.0
    expr = DivisionExpression([num, child])
    poly = ComponentMap()
    poly[num] = PolynomialDegree(0)
    poly[child] = den_degree
    matched, result = visitor.visit_expression(expr, poly)
    assert matched
    assert not result.is_polynomial()

View more ways to use Pyomo

Pyomo Overview

Pyomo is a Python-based open-source software package that supports a diverse set of optimization capabilities for formulating and analyzing optimization models. Pyomo can be used to define symbolic problems, create concrete problem instances, and solve these instances with standard solvers. Pyomo supports a wide range of problem types, including:

• Linear programming
• Quadratic programming
• Nonlinear programming
• Mixed-integer linear programming
• Mixed-integer quadratic programming
• Mixed-integer nonlinear programming
• Mixed-integer stochastic programming
• Generalized disjunctive programming
• Differential algebraic equations
• Mathematical programming with equilibrium constraints
• Constraint programming

Pyomo supports analysis and scripting within a full-featured programming language. Further, Pyomo has also proven an effective framework for developing high-level optimization and analysis tools. For example, the mpi-sppy package provides generic solvers for stochastic programming. mpi-sppy leverages the fact that Pyomo's modeling objects are embedded within a full-featured high-level programming language, which allows for transparent parallelization of subproblems using Python parallel communication libraries.

• Pyomo Home
• About Pyomo
• Download
• Documentation
• Performance Plots

Pyomo was formerly released as the Coopr software library.

Pyomo is available under the BSD License - see the LICENSE.md file.

Pyomo is currently tested with the following Python implementations:

• CPython: 3.8, 3.9, 3.10, 3.11, 3.12
• PyPy: 3.9

Testing and support policy:

At the time of the first Pyomo release after the end-of-life of a minor Python version, we will remove testing for that Python version.

Installation

PyPI

pip install pyomo

Anaconda

conda install -c conda-forge pyomo

Tutorials and Examples

• Pyomo — Optimization Modeling in Python
• Pyomo Workshop Slides
• Prof. Jeffrey Kantor's Pyomo Cookbook
• The companion notebooks for Hands-On Mathematical Optimization with Python
• Pyomo Gallery

Getting Help

To get help from the Pyomo community ask a question on one of the following:

• Use the #pyomo tag on StackOverflow
• Pyomo Forum

Developers

Pyomo development moved to this repository in June 2016 from Sandia National Laboratories. Developer discussions are hosted by Google Groups.

The Pyomo Development team holds weekly coordination meetings on Tuesdays 12:30 - 14:00 (MT). Please contact wg-pyomo@sandia.gov to request call-in information.

By contributing to this software project, you are agreeing to the following terms and conditions for your contributions:

1. You agree your contributions are submitted under the BSD license.
2. You represent you are authorized to make the contributions and grant the license. If your employer has rights to intellectual property that includes your contributions, you represent that you have received permission to make contributions and grant the required license on behalf of that employer.

Related Packages

See https://pyomo.readthedocs.io/en/latest/related_packages.html.