On parallelizing dual decomposition in stochastic integer programming

For stochastic mixed-integer programs, we revisit the dual decomposition algorithm of Carøe and Schultz from a computational perspective with the aim of its parallelization. We address an important bottleneck of parallel execution by identifying a formulation that permits the parallel solution of th...

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Bibliographic Details
Published in:Operations research letters Vol. 41; no. 3; pp. 252 - 258
Main Authors: Lubin, Miles, Martin, Kipp, Petra, Cosmin G., Sandıkçı, Burhaneddin
Format: Journal Article
Language:English
Published: Elsevier B.V 01.05.2013
Elsevier
Subjects:
Bundle methods
Column generation
Computation
Decomposition
Dual decomposition
Integer programming
Mixed-integer programming
Operations research
Optimization
Parallel computing
Parallel processing
Solvers
Stochastic programming
Stochasticity
Dual decomposition
Column generation
Parallel computing
Bundle methods
Mixed-integer programming
Stochastic programming
ISSN:0167-6377, 1872-7468
Online Access:Get full text
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Summary:For stochastic mixed-integer programs, we revisit the dual decomposition algorithm of Carøe and Schultz from a computational perspective with the aim of its parallelization. We address an important bottleneck of parallel execution by identifying a formulation that permits the parallel solution of the master program by using structure-exploiting interior-point solvers. Our results demonstrate the potential for parallel speedup and the importance of regularization (stabilization) in the dual optimization. Load imbalance is identified as a remaining barrier to parallel scalability.
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ISSN:0167-6377
1872-7468
DOI:10.1016/j.orl.2013.02.003