Add to ORKGOur weakly random additive preconditioners facilitate the solution of linear systems of equa-tions and other fundamental matrix computations. Compared to the popular SVD-based multi-plicative preconditioners, these preconditioners are generated more readily and for a much wider class of input matrices. Furthermore they better preserve matrix structure and sparseness and have a wider range of applications, in particular to linear systems with rectangular coefficient matrices. We study the generation of such preconditioners and their impact on conditioning of the input matrix. Our analysis and experiments show the power of our approach even where w
AbstractThis paper presents a class of preconditioning techniques which exploit rational function ap...
We propose new techniques and algorithms that advance the known methods for a number of fundamental ...
International audienceWe introduce a class of preconditioners for general sparse matrices based on t...
Versus the customary preconditioners, our weakly random ones are generated more readily and for a mu...
AbstractOur randomized additive preconditioners are readily available and regularly facilitate the s...
AbstractWe combine our novel SVD-free additive preconditioning with aggregation and other relevant t...
Effective preconditioners are known for some important but special classes of matrices. In contrast ...
It is well and long known that random matrices tend to be well conditioned, and we em-ploy them to a...
Multiplicative preconditioning is a popular SVD-based techniques for the solution of linear systems ...
With a high probablilty our randomized augmentation of a matrix eliminates its rank defi-ciency and ...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
We introduce a class of preconditioners for general sparse matrices based on the Birkhoff-von Neuman...
It is well known that random matrices tend to be well conditioned, and we employ this property to ad...
. For a sparse linear system Ax = b, preconditioners of the form C = D + L + U , where D is the blo...
AbstractSeeking a basis for the null space of a rectangular and possibly rank deficient and ill cond...
AbstractThis paper presents a class of preconditioning techniques which exploit rational function ap...
We propose new techniques and algorithms that advance the known methods for a number of fundamental ...
International audienceWe introduce a class of preconditioners for general sparse matrices based on t...
Versus the customary preconditioners, our weakly random ones are generated more readily and for a mu...
AbstractOur randomized additive preconditioners are readily available and regularly facilitate the s...
AbstractWe combine our novel SVD-free additive preconditioning with aggregation and other relevant t...
Effective preconditioners are known for some important but special classes of matrices. In contrast ...
It is well and long known that random matrices tend to be well conditioned, and we em-ploy them to a...
Multiplicative preconditioning is a popular SVD-based techniques for the solution of linear systems ...
With a high probablilty our randomized augmentation of a matrix eliminates its rank defi-ciency and ...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
We introduce a class of preconditioners for general sparse matrices based on the Birkhoff-von Neuman...
It is well known that random matrices tend to be well conditioned, and we employ this property to ad...
. For a sparse linear system Ax = b, preconditioners of the form C = D + L + U , where D is the blo...
AbstractSeeking a basis for the null space of a rectangular and possibly rank deficient and ill cond...
AbstractThis paper presents a class of preconditioning techniques which exploit rational function ap...
We propose new techniques and algorithms that advance the known methods for a number of fundamental ...
International audienceWe introduce a class of preconditioners for general sparse matrices based on t...