Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments

In this era of large-scale data, distributed systems built on top of clusters of commodity hardware provide cheap and reliable storage and scalable processing of massive data. With cheap storage, instead of storing only currently relevant data, it is common to store as much data as possible, hoping...

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Veröffentlicht in:	Proceedings of the IEEE 2016-01, Vol.104 (1), p.58-92
Hauptverfasser:	Yang, Jiyan, Meng, Xiangrui, Mahoney, Michael W.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithm design and analysis Algorithms Approximation algorithms Approximation methods Big data Computation Computer networks Construction Data processing Design engineering Design factors Distributed databases distributed matrix algorithms Floating point arithmetic least absolute deviation least squares Machine learning algorithms Mathematical models Matrix decomposition preconditioning randomized linear algebra subspace embedding
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