A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty

Knueven, Bernard; Mildebrath, David; Muir, Christopher; Siirola, John D.; Watson, Jean-Paul; Woodruff, David L.

doi:10.1007/s12532-023-00247-3

A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty

Journal Article · Mon Aug 14 00:00:00 EDT 2023 · Mathematical Programming Computation

DOI:https://doi.org/10.1007/s12532-023-00247-3· OSTI ID:1998620

Knueven, Bernard ^[1]; Mildebrath, David ^[2]; Muir, Christopher ^[3]; Siirola, John D. ^[4]; Watson, Jean-Paul ^[5]; Woodruff, David L. ^[6]

National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Rice Univ., Houston, TX (United States)
Georgia Institute of Technology, Atlanta, GA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Univ. of California, Davis, CA (United States)

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. Finally, 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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC36-08GO28308; AC52-07NA27344; NA0003525

OSTI ID:: 1998620

Alternate ID(s):: OSTI ID: 2440324

Report Number(s):: LLNL--JRNL-869093; NREL/JA-2C00-84450; MainId:85223; UUID:9e8eac8a-4f40-4eda-bda6-39efe9a4091f; MainAdminID:68994

Journal Information:: Mathematical Programming Computation, Journal Name: Mathematical Programming Computation Journal Issue: 4 Vol. 15; ISSN 1867-2949

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

References (21)

Modeling with Stochastic Programming King, Alan J.; Wallace, Stein W. Springer Series in Operations Research and Financial Engineering https://doi.org/10.1007/978-0-387-87817-1	book	January 2012
Pyomo – Optimization Modeling in Python Hart, William E.; Laird, Carl; Watson, Jean-Paul Springer Optimization and Its Applications https://doi.org/10.1007/978-1-4614-3226-5	book	January 2012
Partitioning procedures for solving mixed-variables programming problems Benders, J. F. Numerische Mathematik, Vol. 4, Issue 1 https://doi.org/10.1007/BF01386316	journal	December 1962
Cut sharing for multistage stochastic linear programs with interstage dependency Infanger, Gerd; Morton, David P. Mathematical Programming, Vol. 75, Issue 2 https://doi.org/10.1007/BF02592154	journal	November 1996
A note on the selection of Benders’ cuts Fischetti, Matteo; Salvagnin, Domenico; Zanette, Arrigo Mathematical Programming, Vol. 124, Issue 1-2 https://doi.org/10.1007/s10107-010-0365-7	journal	May 2010
Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs Gade, Dinakar; Hackebeil, Gabriel; Ryan, Sarah M. Mathematical Programming, Vol. 157, Issue 1 https://doi.org/10.1007/s10107-016-1000-z	journal	April 2016
A cross-decomposition scheme with integrated primal–dual multi-cuts for two-stage stochastic programming investment planning problems Mitra, Sumit; Garcia-Herreros, Pablo; Grossmann, Ignacio E. Mathematical Programming, Vol. 157, Issue 1 https://doi.org/10.1007/s10107-016-1001-y	journal	April 2016
Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems Watson, Jean-Paul; Woodruff, David L. Computational Management Science, Vol. 8, Issue 4 https://doi.org/10.1007/s10287-010-0125-4	journal	July 2010
Pyomo: modeling and solving mathematical programs in Python Hart, William E.; Watson, Jean-Paul; Woodruff, David L. Mathematical Programming Computation, Vol. 3, Issue 3 https://doi.org/10.1007/s12532-011-0026-8	journal	August 2011
Toward scalable stochastic unit commitment: Part 2: solver configuration and performance assessment Cheung, Kwok; Gade, Dinakar; Silva-Monroy, César Energy Systems, Vol. 6, Issue 3 https://doi.org/10.1007/s12667-015-0148-6	journal	April 2015
Scenario grouping in a progressive hedging-based meta-heuristic for stochastic network design Crainic, Teodor Gabriel; Hewitt, Mike; Rei, Walter Computers & Operations Research, Vol. 43 https://doi.org/10.1016/j.cor.2013.08.020	journal	March 2014
Selection of an optimal subset of sizes Jorjani, Soheila; Scott, Carlton H.; Woodruff, David L. International Journal of Production Research, Vol. 37, Issue 16 https://doi.org/10.1080/002075499189998	journal	November 1999
A Frank–Wolfe Progressive Hedging Algorithm for Improved Lower Bounds in Stochastic SCUC Palani, Ananth M.; Wu, Hongyu; Morcos, Medhat M. IEEE Access, Vol. 7 https://doi.org/10.1109/ACCESS.2019.2927346	journal	January 2019
mpi4py: Status Update After 12 Years of Development Dalcin, Lisandro; Fang, Yao-Lung L. Computing in Science & Engineering, Vol. 23, Issue 4 https://doi.org/10.1109/MCSE.2021.3083216	journal	July 2021
L -Shaped Linear Programs with Applications to Optimal Control and Stochastic Programming Van Slyke, R. M.; Wets, Roger SIAM Journal on Applied Mathematics, Vol. 17, Issue 4 https://doi.org/10.1137/0117061	journal	July 1969
Combining Progressive Hedging with a Frank--Wolfe Method to Compute Lagrangian Dual Bounds in Stochastic Mixed-Integer Programming Boland, Natashia; Christiansen, Jeffrey; Dandurand, Brian SIAM Journal on Optimization, Vol. 28, Issue 2 https://doi.org/10.1137/16M1076290	journal	January 2018
`SDDP.jl` : A Julia Package for Stochastic Dual Dynamic Programming Dowson, Oscar; Kapelevich, Lea INFORMS Journal on Computing, Vol. 33, Issue 1 https://doi.org/10.1287/ijoc.2020.0987	journal	January 2021
Efficient Stochastic Programming in Julia Biel, Martin; Johansson, Mikael INFORMS Journal on Computing, Vol. 34, Issue 4 https://doi.org/10.1287/ijoc.2022.1158	journal	July 2022
NETGEN: A Program for Generating Large Scale Capacitated Assignment, Transportation, and Minimum Cost Flow Network Problems Klingman, D.; Napier, A.; Stutz, J. Management Science, Vol. 20, Issue 5 https://doi.org/10.1287/mnsc.20.5.814	journal	January 1974
Scenarios and Policy Aggregation in Optimization Under Uncertainty Rockafellar, R. T.; Wets, Roger J. -B. Mathematics of Operations Research, Vol. 16, Issue 1 https://doi.org/10.1287/moor.16.1.119	journal	February 1991
Mape Maker: A Scenario Creator. Watson, Jean-Paul; Goujard, Guillaume; Woodruff, David https://doi.org/10.2172/1561444	report	September 2019

Similar Records

mpi-sppy: Optimization Under Uncertainty for Pyomo

Conference · Sun Nov 15 23:00:00 EST 2020 · OSTI ID:1721736

Parallel computing for power system climate resiliency: Solving a large-scale stochastic capacity expansion problem with mpi-sppy

Journal Article · Sun Jun 30 20:00:00 EDT 2024 · Electric Power Systems Research · OSTI ID:2438189

Related Subjects

97 MATHEMATICS AND COMPUTING
decomposition strategies
parallel computing
progressive hedging
stochastic programming

A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty

Citation Formats

References (21)

Similar Records

Related Subjects