Tuesday, 13:45 - 14:10 h, Room: H 1028


Martin Takac
Distributed block coordinate descent method: Iteration complexity and efficient hybrid implementation

Coauthors: Jakub Marecek, Peter Richtarik


In this work we propose solving huge-scale instances of regularized convex minimization problems using a distributed block coordinate descent method. We analyze the iteration complexity of the (synchronous) algorithm and show how it depends on the way the problem data is partitioned to the nodes. Several variations of the basic method are obtained based on the way updates are handled (P2P, broadcasting, asynchronicity). Finally, we report encouraging numerical results for an efficient hybrid MPI + Open MP implementation applied to LASSO and sparse support vector machine instances.


Talk 2 of the invited session Tue.2.H 1028
"Coordinate descent methods for huge-scale optimization" [...]
Cluster 21
"Sparse optimization & compressed sensing" [...]


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