Abstract. In this paper, we consider a class of hierarchically rank structured matrices that includes some of the hierarchical matrices occurring in the literature, such as hierarchically semiseparable (HSS) and certain H2-matrices. We describe a fast (O(r3n log(n))) and stable algorithm to transform this hierarchical representation into a so-called unitary-weight representation, as introduced in an earlier work of the authors. This reduction allows the use of fast and stable unitary-weight routines (or by the same means, fast and stable routines for sequentially semiseparable (SSS) and quasiseparable representations used by other authors in the literature), leading, e.g, to direct methods for linear system solution and for the computation ...
Eigenvalue computations for structured rank matrices are the subject of many investigations nowadays...
The standard algorithms for dense matrices become expensive for large matrices, since the number of ...
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use...
In this paper we consider a class of hierarchically rank structured matrices, including some of the ...
In this thesis, we study a important class of structured matrices: "Hierarchically Semi-Separable" m...
Abstract. Randomized sampling has recently been proven a highly efficient technique for computing ap...
This dissertation presents several fast and stable algorithms for both dense and sparse matrices bas...
Abstract. This paper proposes a new type of iteration for computing eigenvalues of semiseparable (pl...
The hierarchical (H-) matrix format allows storing a variety of dense matrices from certain applicat...
Abstract — In this paper, we study an important class of struc-tured matrices: ”Hierarchically Semi-...
AbstractIn this paper we describe how to compute the eigenvalues of a unitary rank structured matrix...
This PhD thesis is an important development in the theories, methods, and applications of eigenvalue...
This self-contained monograph presents matrix algorithms and their analysis. The new technique enabl...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
We develop a hierarchical matrix construction algorithm using matrix-vector multiplications, based o...
Eigenvalue computations for structured rank matrices are the subject of many investigations nowadays...
The standard algorithms for dense matrices become expensive for large matrices, since the number of ...
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use...
In this paper we consider a class of hierarchically rank structured matrices, including some of the ...
In this thesis, we study a important class of structured matrices: "Hierarchically Semi-Separable" m...
Abstract. Randomized sampling has recently been proven a highly efficient technique for computing ap...
This dissertation presents several fast and stable algorithms for both dense and sparse matrices bas...
Abstract. This paper proposes a new type of iteration for computing eigenvalues of semiseparable (pl...
The hierarchical (H-) matrix format allows storing a variety of dense matrices from certain applicat...
Abstract — In this paper, we study an important class of struc-tured matrices: ”Hierarchically Semi-...
AbstractIn this paper we describe how to compute the eigenvalues of a unitary rank structured matrix...
This PhD thesis is an important development in the theories, methods, and applications of eigenvalue...
This self-contained monograph presents matrix algorithms and their analysis. The new technique enabl...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
We develop a hierarchical matrix construction algorithm using matrix-vector multiplications, based o...
Eigenvalue computations for structured rank matrices are the subject of many investigations nowadays...
The standard algorithms for dense matrices become expensive for large matrices, since the number of ...
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use...