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On a Randomized Multi-Block ADMM for Solving Selected Machine Learning Problems
[article]
2020
arXiv
pre-print
The Alternating Direction Method of Multipliers (ADMM) has now days gained tremendous attentions for solving large-scale machine learning and signal processing problems due to the relative simplicity. However, the two-block structure of the classical ADMM still limits the size of the real problems being solved. When one forces a more-than-two-block structure by variable-splitting, the convergence speed slows down greatly as observed in practice. Recently, a randomly assembled cyclic multi-block
arXiv:1907.01995v2
fatcat:4n5xvj6v3zbs3c3dwg2rxbhc74