In previous papers hierarchical matrices were introduced which are data-sparse and allow an approximate matrix arithmetic of nearly optimal complexity. In this paper we analyse the complexity (storage, addition, multiplication and inversion) of the hierarchical matrix arithmetics. Two criteria, the sparsity and idempotency, are sufficient to give the desired bounds. For standard finite element and boundary element applications we present a construction of the hierarchical matrix format for which we can give explicit bounds for the sparsity and idempotency
International audienceHierarchical matrices (H-matrices) have become important in applications where...
Abstract. Randomized sampling has recently been proven a highly efficient technique for computing ap...
This thesis is on the numerical computation of eigenvalues of symmetric hierarchical matrices. The n...
AbstractIn a preceding paper (Hackbusch, Computing 62 (1999) 89–108), a class of matrices (H-matrice...
summary:We give a short introduction to a method for the data-sparse approximation of matrices resul...
Hierarchical matrix (H-matrix) techniques can be used to efficiently treat dense matrices. With an H...
The hierarchical (H-) matrix format allows storing a variety of dense matrices from certain applicat...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
The multiplication of matrices is an important arithmetic operation in computational mathematics. In...
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...
This self-contained monograph presents matrix algorithms and their analysis. The new technique enabl...
Abstract. In this paper, we consider a class of hierarchically rank structured matrices that include...
The standard algorithms for dense matrices become expensive for large matrices, since the number of ...
International audienceMatrices possessing a low-rank property arise in numerous scientific applicati...
International audienceHierarchical matrices (H-matrices) have become important in applications where...
Abstract. Randomized sampling has recently been proven a highly efficient technique for computing ap...
This thesis is on the numerical computation of eigenvalues of symmetric hierarchical matrices. The n...
AbstractIn a preceding paper (Hackbusch, Computing 62 (1999) 89–108), a class of matrices (H-matrice...
summary:We give a short introduction to a method for the data-sparse approximation of matrices resul...
Hierarchical matrix (H-matrix) techniques can be used to efficiently treat dense matrices. With an H...
The hierarchical (H-) matrix format allows storing a variety of dense matrices from certain applicat...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
The multiplication of matrices is an important arithmetic operation in computational mathematics. In...
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...
This self-contained monograph presents matrix algorithms and their analysis. The new technique enabl...
Abstract. In this paper, we consider a class of hierarchically rank structured matrices that include...
The standard algorithms for dense matrices become expensive for large matrices, since the number of ...
International audienceMatrices possessing a low-rank property arise in numerous scientific applicati...
International audienceHierarchical matrices (H-matrices) have become important in applications where...
Abstract. Randomized sampling has recently been proven a highly efficient technique for computing ap...
This thesis is on the numerical computation of eigenvalues of symmetric hierarchical matrices. The n...