Mathematical Programming Computation

, Volume 10, Issue 2, pp 187–223 | Cite as

pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations

  • Bethany Nicholson
  • John D. Siirola
  • Jean-Paul Watson
  • Victor M. Zavala
  • Lorenz T. Biegler
Full Length Paper
  • 72 Downloads

Abstract

We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differential equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.

Keywords

Dynamic optimization Mathematical modeling Algebraic modeling language DAE constrained optimization PDE constrained optimization 

Mathematics Subject Classification

49M37 68N15 90-04 93A30 90C90 

Notes

Acknowledgements

We thank Carl D. Laird for useful technical discussions and for providing the disease transmission model. Victor M. Zavala acknowledges funding from the U.S. Department of Energy Early Career program. The research was supported in part by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program under contract number KJ0401000 through the Project “Multifaceted Mathematics for Complex Energy Systems”. Sandia is a multi-program laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under Contract DE-AC04-94-AL85000.

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Copyright information

© Springer-Verlag Berlin Heidelberg and The Mathematical Programming Society 2017

Authors and Affiliations

  • Bethany Nicholson
    • 1
  • John D. Siirola
    • 2
  • Jean-Paul Watson
    • 2
  • Victor M. Zavala
    • 3
  • Lorenz T. Biegler
    • 1
  1. 1.Department of Chemical EngineeringCarnegie Mellon UniversityPittsburghUSA
  2. 2.Center for Computing ResearchSandia National LaboratoriesAlbuquerqueUSA
  3. 3.Department of Chemical and Biological EngineeringUniversity of Wisconsin-MadisonMadisonUSA

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