Parallel stochastic gradient algorithms for large-scale matrix completion

This paper develops Jellyfish , an algorithm for solving data-processing problems with matrix-valued decision variables regularized to have low rank. Particular examples of problems solvable by Jellyfish include matrix completion problems and least-squares problems regularized by the nuclear norm or...

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Veröffentlicht in:	Mathematical programming computation Jg. 5; H. 2; S. 201 - 226
Hauptverfasser:	Recht, Benjamin, Ré, Christopher
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer-Verlag 01.06.2013
Schlagworte:	Full Length Paper Mathematics Mathematics and Statistics Mathematics of Computing Operations Research/Decision Theory Optimization Theory of Computation Multicore Parallel computing 90C25 Incremental gradient methods 90C06 90C15 Matrix completion
ISSN:	1867-2949, 1867-2957
Online-Zugang:	Volltext
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