In this thesis, we study a important class of structured matrices: "Hierarchically Semi-Separable" matrices, for which an efficient hierarchically state based representation called Hierarchically Semi- Separable (HSS) representation can be used to represent it with a small amount of parameters that are linear in the dimension of the matrix. Under the framework of this HSS representation, efficient matrix transformation algorithms that are linear in the number of equations are given. In particular, a system Ax = b can be solved with linear complexity. Also, LU and URV factorization can be efficiently executed. There is a close connection between HSS matrices and classical Semi Separable matrices (sometimes called Sequentially Semi Separable ...
AbstractMatrices represented as a sum of diagonal and semiseparable ones are considered here. These ...
Abstract. We present a fast algorithm for linear least squares problems governed by hierarchi-cally ...
This PhD thesis is an important development in the theories, methods, and applications of eigenvalue...
Abstract — In this paper, we study an important class of struc-tured matrices: ”Hierarchically Semi-...
Abstract. In this paper, we consider a class of hierarchically rank structured matrices that include...
In this paper we consider a class of hierarchically rank structured matrices, including some of the ...
Abstract. Randomized sampling has recently been proven a highly efficient technique for computing ap...
Many problems in mathematical physics and engineering involve solving linear systems Ax = b which ar...
This dissertation presents several fast and stable algorithms for both dense and sparse matrices bas...
Hierarchically semiseparable (HSS) matrix algorithms are emerging techniques in constructing the sup...
Abstract. An extended sequentially semiseparable (SSS) representation derived from time-varying syst...
This self-contained monograph presents matrix algorithms and their analysis. The new technique enabl...
We present a distributed-memory library for computations with dense structured matrices. A matrix is...
Quasi-separable matrices are a class of rank-structured matriceswidely used in numerical linear alge...
International audienceThe class of quasiseparable matrices is defined by the property that any subma...
AbstractMatrices represented as a sum of diagonal and semiseparable ones are considered here. These ...
Abstract. We present a fast algorithm for linear least squares problems governed by hierarchi-cally ...
This PhD thesis is an important development in the theories, methods, and applications of eigenvalue...
Abstract — In this paper, we study an important class of struc-tured matrices: ”Hierarchically Semi-...
Abstract. In this paper, we consider a class of hierarchically rank structured matrices that include...
In this paper we consider a class of hierarchically rank structured matrices, including some of the ...
Abstract. Randomized sampling has recently been proven a highly efficient technique for computing ap...
Many problems in mathematical physics and engineering involve solving linear systems Ax = b which ar...
This dissertation presents several fast and stable algorithms for both dense and sparse matrices bas...
Hierarchically semiseparable (HSS) matrix algorithms are emerging techniques in constructing the sup...
Abstract. An extended sequentially semiseparable (SSS) representation derived from time-varying syst...
This self-contained monograph presents matrix algorithms and their analysis. The new technique enabl...
We present a distributed-memory library for computations with dense structured matrices. A matrix is...
Quasi-separable matrices are a class of rank-structured matriceswidely used in numerical linear alge...
International audienceThe class of quasiseparable matrices is defined by the property that any subma...
AbstractMatrices represented as a sum of diagonal and semiseparable ones are considered here. These ...
Abstract. We present a fast algorithm for linear least squares problems governed by hierarchi-cally ...
This PhD thesis is an important development in the theories, methods, and applications of eigenvalue...