Randomized methods for low-rank approximation of matrices and tensors

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• Yuji Nakatsukasa (University of Oxford)
• Thursday 12 October 2023, 15:00-16:00
• Centre for Mathematical Sciences, MR14.

If you have a question about this talk, please contact Nicolas Boulle.

Among the most exciting recent developments in numerical linear algebra is the advent of randomized algorithms that are fast, scalable, robust, and reliable. Low-rank approximation is among the most significant problems for which randomization has had a significant impact. In this talk I will first review some of the most successful randomized algorithms for low-rank approximation of matrices. I will then turn to tensors, and describe an algorithm RTSMS (Randomized Tucker with single-mode sketching) for an approximate Tucker decomposition. RTSMS only sketches one mode at a time, so the sketch matrices are significantly smaller than alternative approaches, and RTSMS can outperform existing methods by a large margin. RTSMS is a joint work with Behnam Hashemi (Leicester).

This talk is part of the Applied and Computational Analysis series.

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