Iterative Randomized Algorithms for Low Rank Approximation of Tera-scale Matrices with Small Spectral Gaps

2018 IEEE/ACM 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (scalA), pp.33-40, (2018)

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Abstract:

Randomized approaches for low rank matrix approximations have become popular in recent years and often offer significant advantages over classical algorithms because of their scalability and numerical robustness on distributed memory platforms. We present a distributed implementation of randomized block iterative methods to compute low ra...More

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