summary:We give a short introduction to a method for the data-sparse approximation of matrices resulting from the discretisation of non-local operators occurring in boundary integral methods or as the inverses of partial differential operators. The result of the approximation will be the so-called hierarchical matrices (or short $\mathcal {H}$-matrices). These matrices form a subset of the set of all matrices and have a data-sparse representation. The essential operations for these matrices (matrix-vector and matrix-matrix multiplication, addition and inversion) can be performed in, up to logarithmic factors, optimal complexity
The standard algorithms for dense matrices become expensive for large matrices, since the number of ...
Abstract. In this paper, we consider a class of hierarchically rank structured matrices that include...
We develop a hierarchical matrix construction algorithm using matrix-vector multiplications, based o...
summary:We give a short introduction to a method for the data-sparse approximation of matrices resul...
In previous papers hierarchical matrices were introduced which are data-sparse and allow an approxim...
This self-contained monograph presents matrix algorithms and their analysis. The new technique enabl...
AbstractMany of today’s most efficient numerical methods are based on multilevel decompositions: The...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
Discretizing an integral operator by a standard finite element or boundary element method typically ...
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...
A class of hierarchical matrices (H-matrices) allows the data-sparse approximation to integral and m...
This thesis is focused on using low rank matrices in numerical mathematics. We introduce conjugate g...
Hierarchical matrix (H-matrix) techniques can be used to efficiently treat dense matrices. With an H...
AbstractIn a preceding paper (Hackbusch, Computing 62 (1999) 89–108), a class of matrices (H-matrice...
The standard algorithms for dense matrices become expensive for large matrices, since the number of ...
Abstract. In this paper, we consider a class of hierarchically rank structured matrices that include...
We develop a hierarchical matrix construction algorithm using matrix-vector multiplications, based o...
summary:We give a short introduction to a method for the data-sparse approximation of matrices resul...
In previous papers hierarchical matrices were introduced which are data-sparse and allow an approxim...
This self-contained monograph presents matrix algorithms and their analysis. The new technique enabl...
AbstractMany of today’s most efficient numerical methods are based on multilevel decompositions: The...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
Discretizing an integral operator by a standard finite element or boundary element method typically ...
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...
A class of hierarchical matrices (H-matrices) allows the data-sparse approximation to integral and m...
This thesis is focused on using low rank matrices in numerical mathematics. We introduce conjugate g...
Hierarchical matrix (H-matrix) techniques can be used to efficiently treat dense matrices. With an H...
AbstractIn a preceding paper (Hackbusch, Computing 62 (1999) 89–108), a class of matrices (H-matrice...
The standard algorithms for dense matrices become expensive for large matrices, since the number of ...
Abstract. In this paper, we consider a class of hierarchically rank structured matrices that include...
We develop a hierarchical matrix construction algorithm using matrix-vector multiplications, based o...