Add to ORKGWe propose new techniques and algorithms that advance the known methods for a number of fundamental problems of matrix computations. These problems includes approximation of leading and trailing singular spaces of a matrix with extensions to derandomized approxima-tion by low-rank matrices and by structured matrices, support for numerically safe Gaussian elimination with no pivoting, and devising effective preconditioners that cover the general class of matrices having a small numerical nullity or a small numerical rank. Our technical novelties include randomized additive preconditioning and augmentation for general and structured ma-trices, derandomization, and dual extension of the Sherman–Morrison–Woodbury formula. Our extensive tests de...
Random matrices tend to be well conditioned, and so one can expect that appending prop-erly scaled r...
AbstractSeeking a basis for the null space of a rectangular and possibly rank deficient and ill cond...
This work explores how randomization can be exploited to deliver sophisticated algorithms with prova...
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...
It is well known that random matrices tend to be well conditioned, and we employ this property to ad...
A random matrix is likely to be well conditioned, and motivated by this well known property we emplo...
The purpose of this text is to provide an accessible introduction to a set of recently developed alg...
With a high probablilty our randomized augmentation of a matrix eliminates its rank defi-ciency and ...
Random matrices tend to be well conditioned, and we employ this well known property to advance matri...
Our weakly random additive preconditioners facilitate the solution of linear systems of equa-tions a...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill condi-tioned...
AbstractOur randomized additive preconditioners are readily available and regularly facilitate the s...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill con-ditioned...
Effective preconditioners are known for some important but special classes of matrices. In contrast ...
Random matrices tend to be well conditioned, and so one can expect that appending prop-erly scaled r...
AbstractSeeking a basis for the null space of a rectangular and possibly rank deficient and ill cond...
This work explores how randomization can be exploited to deliver sophisticated algorithms with prova...
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...
It is well known that random matrices tend to be well conditioned, and we employ this property to ad...
A random matrix is likely to be well conditioned, and motivated by this well known property we emplo...
The purpose of this text is to provide an accessible introduction to a set of recently developed alg...
With a high probablilty our randomized augmentation of a matrix eliminates its rank defi-ciency and ...
Random matrices tend to be well conditioned, and we employ this well known property to advance matri...
Our weakly random additive preconditioners facilitate the solution of linear systems of equa-tions a...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill condi-tioned...
AbstractOur randomized additive preconditioners are readily available and regularly facilitate the s...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill con-ditioned...
Effective preconditioners are known for some important but special classes of matrices. In contrast ...
Random matrices tend to be well conditioned, and so one can expect that appending prop-erly scaled r...
AbstractSeeking a basis for the null space of a rectangular and possibly rank deficient and ill cond...
This work explores how randomization can be exploited to deliver sophisticated algorithms with prova...