Add to ORKGOur randomized preprocessing of a matrix by means of augmentation counters its degeneracy and ill conditioning, uses neither pivoting nor orthogonalization, readily preserves matrix struc-ture and sparseness, and leads to dramatic speedup of the solution of general and structured linear systems of equations in terms of both estimated arithmetic time and observed CPU time
It is well and long known that random matrices tend to be well conditioned, and we em-ploy them to a...
The present thesis focuses on the design and analysis of randomized algorithms for accelerating seve...
International audienceWe illustrate how linear algebra calculations can be enhanced by statistical t...
With a high probablilty our randomized augmentation of a matrix eliminates its rank defi-ciency and ...
Effective preconditioners are known for some important but special classes of matrices. In contrast ...
AbstractOur randomized preprocessing enables pivoting-free and orthogonalization-free solution of ho...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill condi-tioned...
AbstractSeeking a basis for the null space of a rectangular and possibly rank deficient and ill cond...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill con-ditioned...
Solution of homogeneous linear systems of equations is a basic operation of matrix computa-tions. Th...
AbstractSolution of homogeneous linear systems of equations is a basic operation of matrix computati...
By combining our weakly randomized preconditioning with aggrega-tion and other known and novel techn...
We illustrate how linear algebra calculations can be enhanced by statistical techniques in the case ...
Our weakly random additive preconditioners facilitate the solution of linear systems of equa-tions a...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
It is well and long known that random matrices tend to be well conditioned, and we em-ploy them to a...
The present thesis focuses on the design and analysis of randomized algorithms for accelerating seve...
International audienceWe illustrate how linear algebra calculations can be enhanced by statistical t...
With a high probablilty our randomized augmentation of a matrix eliminates its rank defi-ciency and ...
Effective preconditioners are known for some important but special classes of matrices. In contrast ...
AbstractOur randomized preprocessing enables pivoting-free and orthogonalization-free solution of ho...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill condi-tioned...
AbstractSeeking a basis for the null space of a rectangular and possibly rank deficient and ill cond...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill con-ditioned...
Solution of homogeneous linear systems of equations is a basic operation of matrix computa-tions. Th...
AbstractSolution of homogeneous linear systems of equations is a basic operation of matrix computati...
By combining our weakly randomized preconditioning with aggrega-tion and other known and novel techn...
We illustrate how linear algebra calculations can be enhanced by statistical techniques in the case ...
Our weakly random additive preconditioners facilitate the solution of linear systems of equa-tions a...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
It is well and long known that random matrices tend to be well conditioned, and we em-ploy them to a...
The present thesis focuses on the design and analysis of randomized algorithms for accelerating seve...
International audienceWe illustrate how linear algebra calculations can be enhanced by statistical t...