1 citations · 2 across the 24 of their papers we have counts for
10 papers · 1 filter
A preconditioned inverse iteration with an improved convergence guarantee
Foivos Alimisis, Daniel Kressner, Nian Shao +1
Preconditioned eigenvalue solvers offer the possibility to incorporate preconditioners for the solution of large-scale eigenvalue problems, as they arise from the discretization of…
Adaptive randomized pivoting for column subset selection, DEIM, and low-rank approximation
Alice Cortinovis, Daniel Kressner
We derive a new adaptive leverage score sampling strategy for solving the Column Subset Selection Problem (CSSP). The resulting algorithm, called Adaptive Randomized Pivoting, can…
Randomized methods for computing joint eigenvalues, with applications to multiparameter eigenvalue problems and root finding
Haoze He, Daniel Kressner, Bor Plestenjak
It is well known that a family of commuting matrices can be simultaneously triangularized by a unitary similarity transformation. The diagonal entries of the triangular…
Preconditioned Low-Rank Riemannian Optimization for Symmetric Positive Definite Linear Matrix Equations
Ivan Bioli, Daniel Kressner, Leonardo Robol
This work is concerned with the numerical solution of large-scale symmetric positive definite matrix equations of the form $A_1XB_1^\top + A_2XB_2^\top + \dots + A_\ell X B_\ell^\t…
On a randomized small-block Lanczos method for large-scale null space computations
Daniel Kressner, Nian Shao
Computing the null space of a large sparse matrix is a challenging computational problem, especially if the nullity -- the dimension of the null space -- is not small. When app…
Subspace embedding with random Khatri-Rao products and its application to eigensolvers
Zvonimir Bujanović, Luka Grubišić, Daniel Kressner +1
Various iterative eigenvalue solvers have been developed to compute parts of the spectrum for a large sparse matrix, including the power method, Krylov subspace methods, contour in…
A simple, randomized algorithm for diagonalizing normal matrices
Haoze He, Daniel Kressner
We present and analyze a simple numerical method that diagonalizes a complex normal matrix A by diagonalizing the Hermitian matrix obtained from a random linear combination of the…
Randomized Nyström approximation of non-negative self-adjoint operators
David Persson, Nicolas Boullé, Daniel Kressner
The randomized singular value decomposition (SVD) has become a popular approach to computing cheap, yet accurate, low-rank approximations to matrices due to its efficiency and stro…
Computing Functions of Symmetric Hierarchically Semiseparable Matrices
Angelo A. Casulli, Daniel Kressner, Leonardo Robol
The aim of this work is to develop a fast algorithm for approximating the matrix function of a square matrix that is symmetric and has hierarchically semiseparable (HSS)…
A randomized algorithm for simultaneously diagonalizing symmetric matrices by congruence
Haoze He, Daniel Kressner
A family of symmetric matrices is SDC (simultaneous diagonalization by congruence, also called non-orthogonal joint diagonalization) if there is an invertible mat…