We develop a hierarchical matrix construction algorithm using matrix-vector multiplications, based on the randomized singular value decomposition of low-rank matrices. The algorithm uses O(logn) applications of the matrix on structured random test vectors and O(nlogn) extra computational cost, where n is the dimension of the unknown matrix. Numerical examples on constructing Green's functions for elliptic operators in two dimensions show efficiency and accuracy of the proposed algorithm. © 2011 Elsevier Inc
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...
International audienceFor matrices with displacement structure, basic operations like multiplication...
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use...
Abstract. Randomized sampling has recently been proven a highly efficient technique for computing ap...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
A randomized algorithm for computing a data sparse representation of a given rank structured matrix ...
This dissertation presents several fast and stable algorithms for both dense and sparse matrices bas...
Abstract. In this paper, we consider a class of hierarchically rank structured matrices that include...
The multiplication of matrices is an important arithmetic operation in computational mathematics. In...
The multiplication of matrices is an important arithmetic operation in computational mathematics. In...
Many problems in mathematical physics and engineering involve solving linear systems Ax = b which ar...
This self-contained monograph presents matrix algorithms and their analysis. The new technique enabl...
In this paper we consider a class of hierarchically rank structured matrices, including some of the ...
Hierarchical matrix (H-matrix) techniques can be used to efficiently treat dense matrices. With an H...
summary:We give a short introduction to a method for the data-sparse approximation of matrices resul...
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...
International audienceFor matrices with displacement structure, basic operations like multiplication...
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use...
Abstract. Randomized sampling has recently been proven a highly efficient technique for computing ap...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
A randomized algorithm for computing a data sparse representation of a given rank structured matrix ...
This dissertation presents several fast and stable algorithms for both dense and sparse matrices bas...
Abstract. In this paper, we consider a class of hierarchically rank structured matrices that include...
The multiplication of matrices is an important arithmetic operation in computational mathematics. In...
The multiplication of matrices is an important arithmetic operation in computational mathematics. In...
Many problems in mathematical physics and engineering involve solving linear systems Ax = b which ar...
This self-contained monograph presents matrix algorithms and their analysis. The new technique enabl...
In this paper we consider a class of hierarchically rank structured matrices, including some of the ...
Hierarchical matrix (H-matrix) techniques can be used to efficiently treat dense matrices. With an H...
summary:We give a short introduction to a method for the data-sparse approximation of matrices resul...
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...
International audienceFor matrices with displacement structure, basic operations like multiplication...
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use...