pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations
We describe pyomo.dae, an open source Pythonbased modeling framework that enables highlevel 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 highorder differential equations and partial differential equations, defined on restricted domain types, and the ability to automatically transform highlevel abstract models into finitedimensional algebraic problems that can be solved with offtheshelf 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 stateoftheart optimization solvers.
 Authors:

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 Carnegie Mellon Univ., Pittsburgh, PA (United States). Dept. of Chemical Engineering
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States). Center for Computing Research
 Univ. of Wisconsin, Madison, WI (United States). Dept. of Chemical and Biological Engineering
 Publication Date:
 Report Number(s):
 SAND201712346J
Journal ID: ISSN 18672949; PII: 127
 Grant/Contract Number:
 AC0494AL85000; KJ0401000; NA0003525
 Type:
 Accepted Manuscript
 Journal Name:
 Mathematical Programming Computation
 Additional Journal Information:
 Journal Volume: 10; Journal Issue: 2; Journal ID: ISSN 18672949
 Publisher:
 Springer
 Research Org:
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Sponsoring Org:
 USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC21)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; Dynamic optimization; Mathematical modeling; Algebraic modeling language; DAE constrained optimization; PDE constrained optimization
 OSTI Identifier:
 1421609
Nicholson, Bethany, Siirola, John D., Watson, JeanPaul, Zavala, Victor M., and Biegler, Lorenz T.. pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations. United States: N. p.,
Web. doi:10.1007/s1253201701270.
Nicholson, Bethany, Siirola, John D., Watson, JeanPaul, Zavala, Victor M., & Biegler, Lorenz T.. pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations. United States. doi:10.1007/s1253201701270.
Nicholson, Bethany, Siirola, John D., Watson, JeanPaul, Zavala, Victor M., and Biegler, Lorenz T.. 2017.
"pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations". United States.
doi:10.1007/s1253201701270.
@article{osti_1421609,
title = {pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations},
author = {Nicholson, Bethany and Siirola, John D. and Watson, JeanPaul and Zavala, Victor M. and Biegler, Lorenz T.},
abstractNote = {We describe pyomo.dae, an open source Pythonbased modeling framework that enables highlevel 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 highorder differential equations and partial differential equations, defined on restricted domain types, and the ability to automatically transform highlevel abstract models into finitedimensional algebraic problems that can be solved with offtheshelf 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 stateoftheart optimization solvers.},
doi = {10.1007/s1253201701270},
journal = {Mathematical Programming Computation},
number = 2,
volume = 10,
place = {United States},
year = {2017},
month = {12}
}