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

Journal Article · · Mathematical Programming Computation
 [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
+ Show Author Affiliations
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.
Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Grant/Contract Number:
AC04-94AL85000; NA0003525
OSTI ID:
1421609
Alternate ID(s):
OSTI ID: 1324250
Report Number(s):
SAND--2017-12346J; PII: 127
Journal Information:
Mathematical Programming Computation, Journal Name: Mathematical Programming Computation Journal Issue: 2 Vol. 10; ISSN 1867-2949
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (2)

Dynamic real‐time optimization for a CO 2 capture process journal November 2018
Dynamic optimization for gas blending in pipeline networks with gas interchangeability control journal January 2020

Figures / Tables (7)

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Fig. 3(p. 22)figure Fig. 3
Table 2(p. 24)table Table 2
Table 3(p. 26)table Table 3
Fig. 4(p. 27)figure Fig. 4

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Related Subjects

97 MATHEMATICS AND COMPUTING
Algebraic modeling language
DAE constrained optimization
Dynamic optimization
Mathematical modeling
PDE constrained optimization