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mpi-sppy: Optimization Under Uncertainty for Pyomo

  • Bernard Knueven
  • , David Mildebrath
  • , Christopher Muir
  • , John Siirola
  • , David Woodruff
  • , Jean-Paul Watson
  • Rice University
  • Georgia Institute of Technology
  • Sandia National Laboratories
  • University of California at Davis
  • Lawrence Livermore National Laboratory

Research output: NLRPresentation

Abstract

We introduce a new Pyomo extension for optimization under uncertainty, mpi-sppy. This extension allows for the mixing of, and sharing of information between, various algorithmic approaches for optimization under uncertainty. We will discuss the underlying architecture and assess the performance and scalability of mpi-sppy on various systems, including HPC clusters.
Original languageAmerican English
Number of pages16
StatePublished - 2020

Publication series

NamePresented at the INFORMS Annual Meeting 2020, 9-13 November 2020

NLR Publication Number

  • NREL/PR-2C00-78043

Keywords

  • parallel programming
  • stochastic optimization
  • unit commitment

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