Friday, 11:00 - 11:25 h, Room: MA 141


Jia Kang
Parallel solution of structured nonlinear problems using Pyomo and PySP

Coauthors: Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Daniel P. Word


Nonlinear programming has proven to be an effective tool for dynamic optimization, parameter estimation, and nonlinear stochastic programming. However, as problem sizes continue to increase, these problems can exceed the computing capabilities of modern desktop computers using serial solution approaches.
Block structured problems arise in a number of areas, including nonlinear stochastic programming and parameter estimation. Pyomo, an open-source algebraic modeling language, and PySP, a python-based stochastic programming framework, are used to formulate and solve these problems in parallel. In this work, we compare two approaches for parallel solution of these problems. Rockafellar and Wets' progressive hedging algorithm is used to efficiently solve large-scale parameter estimation problems in parallel with IPOPT (a nonlinear interior-point package) used as the sub-problem solver. As well, an internal decomposition approach that solves the structured linear KKT system in parallel is also used. We compare these parallel solution approaches with serial methods and discuss our experience working within Pyomo and PySP.


Talk 2 of the invited session Fri.1.MA 141
"Progressive hedging: Innovations and applications" [...]
Cluster 22
"Stochastic optimization" [...]


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