For questions related to the Python optimization modeling package Pyomo. Pyomo is a Python-based open-source software package that supports a diverse set of optimization capabilities for formulating, solving, and analyzing optimization models. Pyomo allows users to formulate optimization 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.

Pyomo is available for download here. or:

pip install pyomo

A core capability of Pyomo is modeling structured optimization applications. Pyomo can be used to define general symbolic problems, create specific problem instances, and solve these instances using commercial and open-source solvers. Pyomo's modeling objects are embedded within a full-featured high-level programming language providing a rich set of supporting libraries, which distinguishes Pyomo from other algebraic modeling languages like AMPL, AIMMS and GAMS.

Optimization models can be initialized with Python data or external data, and external data sources can be defined using spreadsheets, databases and various formats of text files. Pyomo supports dozens of solvers, both open source and commercial. both open source and commercial, including many solvers supported by AMPL, PICO, CBC, CPLEX, IPOPT, Gurobi and GLPK. Pyomo can either invoke the solver directly or asynchronous 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 nl (format).

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
  • Stochastic programming
  • Generalized disjunctive programming
  • Differential algebraic equations
  • Bilevel programming
  • Mathematical programs with equilibrium constraints

Pyomo also supports iterative analysis and scripting capabilities 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 PySP package provides generic solvers for stochastic programming. PySP 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.

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