8 citations · 34 across the 25 of their papers we have counts for
7 papers · 1 filter
ParaSLRF: A High Performance Rational Filter Method for Solving Large Scale Eigenvalue Problems
Biyi Wang, Karl Meerbergen, Raf Vandebril +2
In \emph{Wang et al., A Shifted Laplace Rational Filter for Large-Scale Eigenvalue Problems}, the SLRF method was proposed to compute all eigenvalues of a symmetric definite genera…
Krylov and core transformation algorithms for an inverse eigenvalue problem to compute recurrences of multiple orthogonal polynomials
Amin Faghih, Michele Rinelli, Marc Van Barel +2
In this paper, we develop algorithms for computing the recurrence coefficients corresponding to multiple orthogonal polynomials on the step-line. We reformulate the problem as an i…
Manifold-valued function approximation from multiple tangent spaces
Hang Wang, Raf Vandebril, Joeri Van der Veken +1
Approximating a manifold-valued function from samples of input-output pairs consists of modeling the relationship between an input from a vector space and an output on a Riemannian…
A shifted Laplace rational filter for large-scale eigenvalue problems
Biyi Wang, Karl Meerbergen, Raf Vandebril +2
We present a rational filter for computing all eigenvalues of a symmetric definite eigenvalue problem lying in an interval on the real axis. The linear systems arising from the fil…
On computing the zeros of a class of Sobolev orthogonal polynomials
Nicola Mastronardi, Marc Van Barel, Raf Vandebril +1
A fast and weakly stable method for computing the zeros of a particular class of hypergeometric polynomials is presented. The studied hypergeometric polynomials satisfy a higher or…
Constructing Sobolev orthonormal rational functions via an updating procedure
Amin Faghih, Marc Van Barel, Niel Van Buggenhout +1
In this paper, we generate the recursion coefficients for rational functions with prescribed poles that are orthonormal with respect to a continuous Sobolev inner product. Using a…
Algorithms for Parallel Shared-Memory Sparse Matrix-Vector Multiplication on Unstructured Matrices
Kobe Bergmans, Karl Meerbergen, Raf Vandebril
The sparse matrix-vector (SpMV) multiplication is an important computational kernel, but it is notoriously difficult to execute efficiently. This paper investigates algorithm perfo…