We present families of algorithms for operations related to the computation of the inverse of a Symmetric Positive Definite (SPD) matrix: Cholesky factorization, inversion of a tr...
Paolo Bientinesi, Brian C. Gunter, Robert A. van d...
The algorithms in the current sequential numerical linear algebra libraries (e.g. LAPACK) do not parallelize well on multicore architectures. A new family of algorithms, the tile a...
Emmanuel Agullo, Henricus Bouwmeester, Jack Dongar...
ABSTRACT. The aim of this paper is to provide a convergence analysis for a preconditioned subspace iteration, which is designated to determine a modest number of the smallest eigen...
Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is perfor...
Gert R. G. Lanckriet, Nello Cristianini, Peter L. ...
We consider the problem of solving a symmetric, positive definite system of linear equations. The most well-known and widely-used method for solving such systems is the preconditi...