4 papers · 1 filter
Randomized Jacobi-Davidson method
Laura Grigori, Taejun Park, Igor Simunec
The Jacobi-Davidson method is a widely used subspace method for computing a few eigenpairs of a large, sparse, non-Hermitian matrix closest to a target. Like other subspace methods…
Restoring similarity in randomized Krylov methods with applications to eigenvalue problems and matrix functions
Laura Grigori, Daniel Kressner, Nian Shao +1
The randomized Arnoldi process has been used in large-scale scientific computing because it produces a well-conditioned basis for the Krylov subspace more quickly than the standard…
Randomized orthogonalization and Krylov subspace methods: principles and algorithms
Jean-Guillaume de Damas, Laura Grigori, Igor Simunec +1
We present an overview of randomized orthogonalization techniques that construct a well-conditioned basis whose sketch is orthonormal. Randomized orthogonalization has recently eme…
Randomized biorthogonalization through a two-sided Gram-Schmidt process
Laura Grigori, Lorenzo Piccinini, Igor Simunec
We propose and analyze a randomized two-sided Gram-Schmidt process for the biorthogonalization of two given matrices . The algorithm aims to find tw…