Add to ORKGThe aim of this thesis is to present new results in randomized matrix computations. Specifically, and ultimately, we show how to modify, or preprocess an ill conditioned matrix having small numerical nullity (co-rank) into a nonsingular well conditioned matrix. This has intrinsic theoretical interest and we show a sample application to accurate solutions of nonsingular and ill conditioned linear systems. We discuss both multiplicative and additive preprocessing; in fact the multiplicative case assists in the derivation of the additive case. In the additive case, we approximate a nonsingular ill conditioned matrix by a singular well conditioned matrix which is then preprocessed into a nonsingular well conditioned matrix, which will also assi...
Randomization of matrix computations has become a hot research area in the big data era. Sampling wi...
A random matrix is likely to be well conditioned, and motivated by this well known property we emplo...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-rev...
The aim of this thesis is to present new results in randomized matrix computations. Specifically, an...
It is well and long known that random matrices tend to be well conditioned, and we em-ploy them to a...
AbstractOur randomized additive preconditioners are readily available and regularly facilitate the s...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill condi-tioned...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill con-ditioned...
AbstractSeeking a basis for the null space of a rectangular and possibly rank deficient and ill cond...
It is well known that random matrices tend to be well conditioned, and we employ this property to ad...
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...
Random matrices tend to be well conditioned, and so one can expect that appending prop-erly scaled r...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-reve...
Randomization of matrix computations has become a hot research area in the big data era. Sampling wi...
A random matrix is likely to be well conditioned, and motivated by this well known property we emplo...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-rev...
The aim of this thesis is to present new results in randomized matrix computations. Specifically, an...
It is well and long known that random matrices tend to be well conditioned, and we em-ploy them to a...
AbstractOur randomized additive preconditioners are readily available and regularly facilitate the s...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill condi-tioned...
Seeking a basis for the null space of a rectangular and possibly rank deficient and ill con-ditioned...
AbstractSeeking a basis for the null space of a rectangular and possibly rank deficient and ill cond...
It is well known that random matrices tend to be well conditioned, and we employ this property to ad...
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...
Random matrices tend to be well conditioned, and so one can expect that appending prop-erly scaled r...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-reve...
Randomization of matrix computations has become a hot research area in the big data era. Sampling wi...
A random matrix is likely to be well conditioned, and motivated by this well known property we emplo...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-rev...