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This content will become publicly available on December 20, 2018

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

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 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.
 [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:
Report Number(s):
Journal ID: ISSN 1867-2949; PII: 127
Grant/Contract Number:
AC04-94AL85000; KJ0401000; NA0003525
Accepted Manuscript
Journal Name:
Mathematical Programming Computation
Additional Journal Information:
Journal Name: Mathematical Programming Computation; Journal ID: ISSN 1867-2949
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)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; Dynamic optimization; Mathematical modeling; Algebraic modeling language; DAE constrained optimization; PDE constrained optimization
OSTI Identifier: