Title slide
Abstract
The matrix eigenvalue problem is often encountered in scientific computing applications. Although it has an uncomplicated problem formulation, the best numerical algorithms devised to solve it are far from obvious. Computing all eigenvalues of a small to medium-sized matrix is nowadays a routine task for an algorithm of implicit QR-type using a bulge chasing technique. In this work we generalize bulge chasing algorithms to pole swapping algorithms. We show that the convergence of pole swapping methods is determined by rational functions and that they can outperform existing methods.
Public PhD defence of dissertation on pole swapping methods for the matrix eigenvalue problem. A high-level overview of the dissertation is included below:
Daan Camps
Researcher in Advanced Technologies Group
My research interests include quantum algorithms, numerical linear algebra, tensor factorization methods and machine learning. I’m particularly interested in studying the interface between HPC and quantum computing.