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 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.
