Modeling Mathematical Programs with Equilibrium Constraints in Pyomo
Abstract
We describe new capabilities for modeling MPEC problems within the Pyomo modeling software. These capabilities include new modeling components that represent complementar ity conditions, modeling transformations for reexpressing models with complementarity con ditions in other forms, and metasolvers that apply transformations and numeric optimization solvers to optimize MPEC problems. We illustrate the breadth of Pyomo's modeling capabil ities for MPEC problems, and we describe how Pyomo's metasolvers can perform local and global optimization of MPEC problems.
 Authors:
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Publication Date:
 Research Org.:
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Sponsoring Org.:
 USDOE National Nuclear Security Administration (NNSA)
 OSTI Identifier:
 1195764
 Report Number(s):
 SAND20155584
594970
 DOE Contract Number:
 AC0494AL85000
 Resource Type:
 Technical Report
 Country of Publication:
 United States
 Language:
 English
Citation Formats
Hart, William E., and Siirola, John Daniel. Modeling Mathematical Programs with Equilibrium Constraints in Pyomo. United States: N. p., 2015.
Web. doi:10.2172/1195764.
Hart, William E., & Siirola, John Daniel. Modeling Mathematical Programs with Equilibrium Constraints in Pyomo. United States. doi:10.2172/1195764.
Hart, William E., and Siirola, John Daniel. 2015.
"Modeling Mathematical Programs with Equilibrium Constraints in Pyomo". United States.
doi:10.2172/1195764. https://www.osti.gov/servlets/purl/1195764.
@article{osti_1195764,
title = {Modeling Mathematical Programs with Equilibrium Constraints in Pyomo},
author = {Hart, William E. and Siirola, John Daniel},
abstractNote = {We describe new capabilities for modeling MPEC problems within the Pyomo modeling software. These capabilities include new modeling components that represent complementar ity conditions, modeling transformations for reexpressing models with complementarity con ditions in other forms, and metasolvers that apply transformations and numeric optimization solvers to optimize MPEC problems. We illustrate the breadth of Pyomo's modeling capabil ities for MPEC problems, and we describe how Pyomo's metasolvers can perform local and global optimization of MPEC problems.},
doi = {10.2172/1195764},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2015,
month = 7
}
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