Abstract
Practical solution of stochastic programming problems generally requires the use of parallel computing resources. Here, we describe the open source package mpi-sppy, in which efficient and scalable parallelization is a central feature. We report computational experiments that demonstrate the ability to solve very large stochastic programming problems - including mixed-integer variants - in minutes of wall clock time, efficiently leveraging significant parallel computing resources. We report results for the largest publicly available instances of stochastic mixed-integer unit commitment problems, solving to provably tight optimality gaps. In addition, we introduce a novel software architecture that facilitates combinations of methods for accelerating convergence that can be combined in plug-and-play manner. The mpi-sppy package is written in Python, leverages the widely used Pyomo (http://www.pyomo.org) library for modeling mathematical programs, builds on existing MPI implementations to ensure efficiency and scalability, and is available via http://github.com/Pyomo/mpi-sppy.
Original language | American English |
---|---|
Pages (from-to) | 591-619 |
Number of pages | 29 |
Journal | Mathematical Programming Computation |
Volume | 15 |
Issue number | 4 |
DOIs | |
State | Published - 2023 |
NREL Publication Number
- NREL/JA-2C00-84450
Keywords
- decomposition strategies
- parallel computing
- progressive hedging
- stochastic programming