Add to ORKGRandom matrices tend to be well conditioned, and we employ this well known property to advance matrix computations. We prove that our algorithms employing Gaussian random matrices are efficient, but in our tests the algorithms have consistently remained as powerful where we used sparse and structured random matrices, defined by much fewer random parame-ters. We numerically stabilize Gaussian elimination with no pivoting as well as block Gaussian elimination, precondition an ill conditioned linear system of equations, compute numerical rank of a matrix without pivoting and orthogonalization, approximate the singular spaces of an ill conditioned matrix associated with its largest and smallest singular values, and approximate this matrix with ...
Randomization of matrix computations has become a hot research area in the big data era. Sampling wi...
With a high probablilty our randomized augmentation of a matrix eliminates its rank defi-ciency and ...
We study two applications of standard Gaussian random multipliers. At first we prove that with a pro...
A random matrix is likely to be well conditioned, and motivated by this well known property we emplo...
It is well known that random matrices tend to be well conditioned, and we employ this property to ad...
It is well and long known that random matrices tend to be well conditioned, and we em-ploy them to a...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
Random matrices tend to be well conditioned, and so one can expect that appending prop-erly scaled r...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill condi-tioned...
We study two applications of standard Gaussian random multipliers. At first we prove that with a pro...
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...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
We prove that standard Gaussian random multipliers are expected to stabilize numerically both Gaussi...
We propose new techniques and algorithms that advance the known methods for a number of fundamental ...
Randomization of matrix computations has become a hot research area in the big data era. Sampling wi...
With a high probablilty our randomized augmentation of a matrix eliminates its rank defi-ciency and ...
We study two applications of standard Gaussian random multipliers. At first we prove that with a pro...
A random matrix is likely to be well conditioned, and motivated by this well known property we emplo...
It is well known that random matrices tend to be well conditioned, and we employ this property to ad...
It is well and long known that random matrices tend to be well conditioned, and we em-ploy them to a...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
Random matrices tend to be well conditioned, and so one can expect that appending prop-erly scaled r...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill condi-tioned...
We study two applications of standard Gaussian random multipliers. At first we prove that with a pro...
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...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
We prove that standard Gaussian random multipliers are expected to stabilize numerically both Gaussi...
We propose new techniques and algorithms that advance the known methods for a number of fundamental ...
Randomization of matrix computations has become a hot research area in the big data era. Sampling wi...
With a high probablilty our randomized augmentation of a matrix eliminates its rank defi-ciency and ...
We study two applications of standard Gaussian random multipliers. At first we prove that with a pro...