Scalability of k-Tridiagonal Matrix Singular Value Decomposition
Andrei Tănăsescu, Mihai Carabaș, Florin Pop, Pantelimon George Popescu · Mathematics · 2021
Singular value decomposition has recently seen a great theoretical improvement for k-tridiagonal matrices, obtaining a considerable speed up over all previous implementations, but at the cost of not ordering the singular values. We provide here a refinement of this method, proving that reordering singular values does not affect performance. We complement our refinement with a scalability study on a real physical cluster setup, offering surprising results. Thus, this method provides a major step up over standard industry implementations.