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Pyomo: Optimization Modeling in Python

Carl Laird, Artie McFerrin Department of Chemical Engineering, Texas A&M University

Abstract:

Mathematical programming has proven to be an efficient tool for design, optimization, and online operation of complex engineered systems. Algebraic modeling languages provide a convenient mechanism for the user to formulate mathematical models and optimization formulations in a language that is similar to the mathematical description of the problem, including constructs for defining sets, expressions, constraints, and objectives. In addition these tools must provide reasonable interface functionality for solvers, for example, first (and possibly second) order derivative information.

Pyomo (Python Optimization Modeling Objects) is a new open-source algebraic optimization language. Pyomo is implemented in Python, and allows the user to make use of extensive scripting capabilities within a familiar, exhaustive, and well-documented programming environment. Pyomo provides general functionality to formulate and solve optimization problems with little or no programming knowledge, but also provides the flexibility to implement high-level language constructs. In this presentation, I will discuss the design and implementation of the Pyomo framework, and give examples of several language extensions including PySP, an extension that supports parallel programming for solution of difficult stochastic programming problems.

Presentation Slides

Biography:Carl Laird, assistant professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University, is holder of the Ruth and William J. Neely ’52 Faculty Fellowship. Dr. Laird's research interests include large-scale nonlinear optimization and parallel scientific computing. Focus areas include chemical process systems, homeland security applications, and large-scale infectious disease spread. Dr. Laird is the recipient of several research and teaching awards, including the prestigious Wilkinson Prize for Numerical Software and the IBM Bravo award for his work on IPOPT, a software library for solving nonlinear, nonconvex, large-scale continuous optimization problems. He also is recipient of the National Science Foundation Faculty Early Development (CAREER) Award and the Montague Center for Teaching Excellence Award. Dr. Laird earned his Ph.D. in Chemical Engineering from Carnegie Mellon in 2006 and his Bachelor of Science in Chemical Engineering from the University of Alberta.