(132g) Pyomo: Python Optimization Modeling Objects

The Python Optimization Modeling Objects (Pyomo) package [1] is an open source tool for modeling optimization applications within Python. Pyomo provides an objected-oriented approach to optimization modeling, and it can be used to define symbolic problems, create concrete problem instances, and solve these instances with standard solvers. While Pyomo provides a capability that is commonly associated with algebraic modeling languages such as AMPL, AIMMS, and GAMS, Pyomo's modeling objects are embedded within a full-featured high-level programming language with a rich set of supporting libraries. Pyomo leverages the capabilities of the Coopr software library [2], which integrates Python packages (including Pyomo) for defining optimizers, modeling optimization applications, and managing computational experiments.

A central design principle within Pyomo is extensibility. Pyomo is built upon a flexible component architecture [3] that allows users and developers to readily extend the core Pyomo functionality. Through these interface points, extensions and applications can have direct access to an optimization model's expression objects. This facilitates the rapid development and implementation of new modeling constructs and as well as high-level solution strategies (e.g. using decomposition- and reformulation-based techniques).

In this presentation, we will give an overview of the Pyomo modeling environment and model syntax, and present several extensions to the core Pyomo environment, including support for Generalized Disjunctive Programming (Coopr GDP), Stochastic Programming (PySP), a generic Progressive Hedging solver [4], and a tailored implementation of Bender's Decomposition.

[1] W.E. Hart, J.-P. Watson, D.L. Woodruff. Python Optimization Modeling Objects (Pyomo). Mathematical Programming Computation (submitted).

[2] Coopr: the COmmon Optimization Python Repository. https://software.sandia.gov/coopr

[3] W.E. Hart and J.D. Siirola.The PyUtilib Component Architecture. The Python Papers (submitted).

[4] J.-P. Watson and D.L. Woodruff. Progressive Hedging Innovations for a Class of Stochastic Mixed-Integer Resource Allocation Problems. Computational Management Science (submitted).