Add to ORKGWe prove that standard Gaussian random multipliers are expected to stabilize numerically both Gaussian elimination with no pivoting and block Gaussian elimination and that they also are expected to support the celebrated randomized algorithm for low-rank approximation of a matrix even without customary oversampling. Our tests show similar results where we apply random circulant and Toeplitz multipliers instead of standard Gaussian ones
It is known that without pivoting Gaussian elimination can run significantly faster, partic-ularly f...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a give...
We study two applications of standard Gaussian random multipliers. At first we prove that with a pro...
We study two applications of standard Gaussian random multipliers. At first we prove that with a pro...
Abstract. We prove that standard Gaussian random multipliers are ex-pected to stabilize numerically ...
A random matrix is likely to be well conditioned, and motivated by this well known property we emplo...
Random matrices tend to be well conditioned, and we employ this well known property to advance matri...
It is well known that random matrices tend to be well conditioned, and we employ this property to ad...
It is known that pivoting-free Gaussian elimination is numerically unsafe but can run signifi-cantly...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
Randomization of matrix computations has become a hot research area in the big data era. Sampling wi...
In the first part of this dissertation, we explore a novel randomized pivoting strategy to efficient...
It is well and long known that random matrices tend to be well conditioned, and we em-ploy them to a...
Abstract. A classical problem in matrix computations is the efficient and reliable approximation of ...
It is known that without pivoting Gaussian elimination can run significantly faster, partic-ularly f...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a give...
We study two applications of standard Gaussian random multipliers. At first we prove that with a pro...
We study two applications of standard Gaussian random multipliers. At first we prove that with a pro...
Abstract. We prove that standard Gaussian random multipliers are ex-pected to stabilize numerically ...
A random matrix is likely to be well conditioned, and motivated by this well known property we emplo...
Random matrices tend to be well conditioned, and we employ this well known property to advance matri...
It is well known that random matrices tend to be well conditioned, and we employ this property to ad...
It is known that pivoting-free Gaussian elimination is numerically unsafe but can run signifi-cantly...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
Randomization of matrix computations has become a hot research area in the big data era. Sampling wi...
In the first part of this dissertation, we explore a novel randomized pivoting strategy to efficient...
It is well and long known that random matrices tend to be well conditioned, and we em-ploy them to a...
Abstract. A classical problem in matrix computations is the efficient and reliable approximation of ...
It is known that without pivoting Gaussian elimination can run significantly faster, partic-ularly f...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a give...