https://github.com/pyomo/pyomo-tutorials

A modern set of Pyomo tutorials
https://github.com/pyomo/pyomo-tutorials

Last synced: 9 months ago
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A modern set of Pyomo tutorials

• Host: GitHub
• URL: https://github.com/pyomo/pyomo-tutorials
• Owner: Pyomo
• License: other
• Created: 2023-10-31T16:35:17.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2025-03-11T22:11:16.000Z (11 months ago)
• Last Synced: 2025-03-30T14:34:33.653Z (10 months ago)
• Language: Python
• Homepage:
• Size: 6.41 MB
• Stars: 19
• Watchers: 9
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Pyomo Tutorials

This repository hosts a modern, updated set of Pyomo tutorials to reflect

the most recent major version's features and offerings. These tutorials are

intended for those new to Pyomo but who have a reasonable understanding of

optimization modeling.

## 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`](https://github.com/Pyomo/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](http://www.pyomo.org)

* [About Pyomo](http://www.pyomo.org/about)

* [Download](http://www.pyomo.org/installation/)

* [Documentation](http://www.pyomo.org/documentation/)

* [Performance Plots](https://pyomo.github.io/performance/)