The computation of, the action of a matrix function on a vector, is a task arising in many
areas of scientific computing. In many applications, the matrix is sparse but so large that only …

Among randomized numerical linear algebra strategies, so‐called sketching procedures are
emerging as effective reduction means to accelerate the computation of Krylov subspace …

Thanks to its great potential in reducing both computational cost and memory requirements,
combining sketching and Krylov subspace techniques has attracted a lot of attention in the …

Current uncertainty quantification is memory and compute expensive, which hinders
practical uptake. To counter, we develop Sketched Lanczos Uncertainty (SLU): an …

Thanks to its great potential in reducing both computational cost and memory requirements,
combining sketching and Krylov subspace techniques has attracted a lot of attention in the …

Truncated LSQR for matrix least squares problems | Computational Optimization and Applications
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While preconditioning is a long-standing concept to accelerate iterative methods for linear
systems, generalizations to matrix functions are still in their infancy. We go a further step in …

We are interested in the numerical solution of the matrix least squares problem min X∈
R^{m× m}∥ AXB+ CXD-F∥ _F, with A and C full column rank, B, D full row rank, F an n× n …

We present a novel application of randomized sketching to the important area of nonlinear
eigenproblems. Our construction is motivated by the recent increase of interest in …