Pyomo is an open-source software package that is used for modeling and solving mathematical problems in operations research (OR) and optimization. It’s based in Python and facilitates the process of defining symbolic problems, solving them using efficient algorithms, and interpreting the results.

Pyomo can be used to define a wide variety of problem types, including linear programming, quadratic programming, nonlinear programming, mixed-integer programming, and more. It’s also capable of interacting with a wide variety of solvers, including both open-source options (such as CBC and GLPK) and commercial options (like Gurobi and CPLEX).

The Pyomo package includes features that make it particularly useful for large and complex optimization problems. For instance, it allows for abstract model formulation, where you define a model in terms of generic components without specifying the exact quantities or coefficients. This is useful for problems where the model structure is known but the specific instances vary. Pyomo also supports scripting, allowing for the automation of model creation and solution processing.

Here is a simple example of a linear programming problem formulated and solved with Pyomo:

The above code defines a linear programming problem with two variables (x and y), an objective function to minimize, and two constraints. It then solves the problem using the GLPK solver and prints the optimal values of the variables.

Please note that you need to install the relevant solver (in this case ‘glpk’) and make it accessible to Pyomo. Pyomo doesn’t come with any solver, it just provides an interface to use different solvers.