Randomized Householder-Cholesky QR Factorization with Multisketching
Linear Algebra seminar by Daniel Szyld, Temple University
Hosted by Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University
Tuesday 10:30 New York (GMT-5)
Recording available
Abstract
Daniel Szyld analyzes rand-cholQR, a randomized method for tall-and-skinny QR factorization using one or two sketch matrices. For numerically full-rank inputs, its orthogonality error is bounded with high probability at the scale of unit roundoff. NVIDIA A100 experiments compare multisketching with CholeskyQR2, reporting comparable or better speed and stronger stability with little additional memory or computation. Joint work with Andrew Higgins, Erik Boman, and Ichitaro Yamazaki.
Topics
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