Randomized numerical linear algebra-RandNLA, for short-concerns the use of
randomization as a resource to develop improved algorithms for large-scale linear algebra …

CholeskyQR2 and shifted CholeskyQR3 are two state-of-the-art algorithms for computing tall-
and-skinny QR factorizations since they attain high performance on current computer …

We present parallel algorithms and data structures for three fundamental operations in
Numerical Linear Algebra:(i) Gaussian and CountSketch random projections and their …

A classical result of Johnson and Lindenstrauss states that a set of $ n $ high dimensional
data points can be projected down to $ O (\log n/\epsilon^ 2) $ dimensions such that the …

We develop a new efficient sequential approximate leverage score algorithm, SALSA, using
methods from randomized numerical linear algebra (RandNLA) for large matrices. We …

Suppose a matrix $ A\in\mathbb {R}^{m\times n} $ of rank $ r $ with singular value
decomposition $ A= U_ {A}\Sigma_ {A} V_ {A}^{T} $, where $ U_ {A}\in\mathbb {R}^{m\times …

In this work, we focus on improving LU-CholeskyQR2\cite {LUChol}. Compared to other
deterministic and randomized CholeskyQR-type algorithms, it does not require a sufficient …

The randomized singular value decomposition proposed in [12] has certainly become one of
the most well-established randomization-based algorithms in numerical linear algebra. The …

Sketching-based preconditioners have been shown to accelerate the solution of dense least-
squares problems with coefficient matrices having substantially more rows than columns …

In this paper, we investigate diagonal estimation for large or implicit matrices, aiming to
develop a novel and efficient stochastic algorithm that incorporates adaptive parameter …