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

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.

Authors:
 [1];  [2];  [2];  [3];  [1]
  1. Carnegie Mellon Univ., Pittsburgh, PA (United States). Dept. of Chemical Engineering
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research
  3. Univ. of Wisconsin, Madison, WI (United States). Dept. of Chemical and Biological Engineering
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1421609
Report Number(s):
SAND-2017-12346J
Journal ID: ISSN 1867-2949; PII: 127
Grant/Contract Number:
AC04-94AL85000; KJ0401000; NA0003525
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Mathematical Programming Computation
Additional Journal Information:
Journal Name: Mathematical Programming Computation; Journal ID: ISSN 1867-2949
Publisher:
Springer
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

Citation Formats

Nicholson, Bethany, Siirola, John D., Watson, Jean-Paul, 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., 2017. Web. doi:10.1007/s12532-017-0127-0.
Nicholson, Bethany, Siirola, John D., Watson, Jean-Paul, 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/s12532-017-0127-0.
Nicholson, Bethany, Siirola, John D., Watson, Jean-Paul, Zavala, Victor M., and Biegler, Lorenz T. Wed . "pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations". United States. doi:10.1007/s12532-017-0127-0.
@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, Jean-Paul and Zavala, Victor M. and Biegler, Lorenz T.},
abstractNote = {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.},
doi = {10.1007/s12532-017-0127-0},
journal = {Mathematical Programming Computation},
number = ,
volume = ,
place = {United States},
year = {Wed Dec 20 00:00:00 EST 2017},
month = {Wed Dec 20 00:00:00 EST 2017}
}

Journal Article:
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