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Title: An Asynchronous Bundle-Trust-Region Method for Dual Decomposition of Stochastic Mixed-Integer Programming

Journal Article · · SIAM Journal on Optimization
DOI:https://doi.org/10.1137/17M1148189· OSTI ID:1497321
 [1]; ORCiD logo [2];  [3]
  1. Argonne National Lab. (ANL), Lemont, IL (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. Univ. of Wisconsin-Madison, Madison, WI (United States)
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We present an asynchronous bundle-trust-region algorithm within the context of Lagrangian dual decomposition for stochastic mixed-integer programs. The approach solves the Lagrangian master problem by using a bundle method with a trust-region constraint. This scheme enables asynchronous computations and can thus help mitigate severe load imbalance issues (associated with the solution of scenario subproblems) and improve parallel efficiency. We provide a convergence analysis and an implementation of the proposed scheme. We also present extensive numerical results on eighty instances of a large-scale stochastic unit commitment problem, and demonstrate that the proposed approach provides significant reductions in solution time and achieves strong scaling.

Research Organization:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
AC52-07NA27344; AC02-06CH11357
OSTI ID:
1497321
Alternate ID(s):
OSTI ID: 1510481
Report Number(s):
LLNL-JRNL-738242; 890992
Journal Information:
SIAM Journal on Optimization, Vol. 29, Issue 1; ISSN 1052-6234
Publisher:
SIAMCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 12 works
Citation information provided by
Web of Science

References (17)

A scenario decomposition algorithm for 0–1 stochastic programs journal November 2013
Incremental proximal methods for large scale convex optimization journal June 2011
Dual decomposition in stochastic integer programming journal February 1999
Benchmarking optimization software with performance profiles journal January 2002
Incremental-like bundle methods with application to energy planning journal September 2009
A Parallel Bundle Framework for Asynchronous Subspace Optimization of Nonsmooth Convex Functions journal January 2014
Generalized Bundle Methods journal January 2002
An Incremental Method for Solving Convex Finite Min-Max Problems journal February 2006
Temporal Decomposition for Improved Unit Commitment in Power System Production Cost Modeling journal September 2018
Data Centers as Dispatchable Loads to Harness Stranded Power journal January 2017
Algorithmic innovations and software for the dual decomposition method applied to stochastic mixed-integer programs journal November 2017
Convergence of Approximate and Incremental Subgradient Methods for Convex Optimization journal January 2004
A Proximal Bundle Method with Approximate Subgradient Linearizations journal January 2006
On parallelizing dual decomposition in stochastic integer programming journal May 2013
Incremental Subgradient Methods for Nondifferentiable Optimization journal January 2001
Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network journal June 2013
Incremental Bundle Methods using Upper Models journal January 2018

Cited By (1)

Asynchronous level bundle methods journal July 2019

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Related Subjects

97 MATHEMATICS AND COMPUTING
stochastic integer programming
bundle method
asynchronous
parallel computing
bundle methods