Abstract
We develop and compare three decomposition algorithms derived from the method of alternating directions. They may be viewed as block Gauss-Seidel variants of augmented Lagrangian approaches that take advantage of block angular structure. From a parallel computation viewpoint, they are ideally suited to a data parallel environment. Numerical results for large-scale multicommodity flow problems are presented to demonstrate the effectiveness of these decomposition algorithmims on the Thinking Machines CM-5 parallel supercomputer relative to the widely-used serial optimization package MINOS 5.4.
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Communicated by O. L. Mangasarian
This material is based on research supported by the Air Force Office of Scientific Research, Grants AFORS-89-0410 and F49620-1-0036, and by NSF Grants CCR-89-07671, CDA-90-24618, and CCR-93-06807. The work of the second author was supported partially by Grant 95.00732.CT01 from the Italian National Research Council (CNR).
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Kontogiorgis, S., De Leone, R. & Meyer, R.R. Alternating direction splitting for block Angular parallel optimization. J Optim Theory Appl 90, 1–29 (1996). https://doi.org/10.1007/BF02192243
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DOI: https://doi.org/10.1007/BF02192243