We consider the approximate computation of spectral projectors for symmetric banded matrices. While this problem has received considerable attention, especially in the context of linear scaling electronic structure methods, the presence of small relative spectral gaps challenges existing methods based on approximate sparsity. In this work, we show how a data-sparse approximation based on hierarchical matrices can be used to overcome this problem. We prove a priori bounds on the approximation error and propose a fast algo-rithm based on the QDWH algorithm, along the works by Nakatsukasa et al. Numerical experiments demonstrate that the performance of our algorithm is robust with respect to the spectral gap. A preliminary MATLAB implementatio...
The ordering of large sparse symmetric matrices for small pro"le and wavefront or for small ban...
We present the first almost-linear time algorithm for constructing linear-sized spectral sparsificat...
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Ci...
In this thesis we address the computation of a spectral decomposition for symmetric banded matrices....
We present a fast algorithm for the construction of a spectral projector. This algorithm allows us t...
We present a fast algorithm for the construction of a spectral projector. This al-gorithm allows us ...
Spectral projectors of Hermitian matrices play a key role in many applications, such as electronic s...
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use...
International audienceMotivated by applications in quantum chemistry and solid state physics, we app...
Abstract. Motivated by applications in quantum chemistry and solid state physics, we apply general r...
AbstractIn this note, we discuss new techniques for analyzing the pseudospectra of matrices and prop...
The projection methods, usually viewed as the methods for computing eigenvalues, can also be used to...
Approximating the singular values or eigenvalues of a matrix, i.e. spectrum approximation, is a fund...
Spectral graph sparsification aims to find an ultra-sparse subgraph whose Laplacian matrix can well ...
The aim of electronic structure calculations is to simulate behavior of complex materials by resolvi...
The ordering of large sparse symmetric matrices for small pro"le and wavefront or for small ban...
We present the first almost-linear time algorithm for constructing linear-sized spectral sparsificat...
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Ci...
In this thesis we address the computation of a spectral decomposition for symmetric banded matrices....
We present a fast algorithm for the construction of a spectral projector. This algorithm allows us t...
We present a fast algorithm for the construction of a spectral projector. This al-gorithm allows us ...
Spectral projectors of Hermitian matrices play a key role in many applications, such as electronic s...
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use...
International audienceMotivated by applications in quantum chemistry and solid state physics, we app...
Abstract. Motivated by applications in quantum chemistry and solid state physics, we apply general r...
AbstractIn this note, we discuss new techniques for analyzing the pseudospectra of matrices and prop...
The projection methods, usually viewed as the methods for computing eigenvalues, can also be used to...
Approximating the singular values or eigenvalues of a matrix, i.e. spectrum approximation, is a fund...
Spectral graph sparsification aims to find an ultra-sparse subgraph whose Laplacian matrix can well ...
The aim of electronic structure calculations is to simulate behavior of complex materials by resolvi...
The ordering of large sparse symmetric matrices for small pro"le and wavefront or for small ban...
We present the first almost-linear time algorithm for constructing linear-sized spectral sparsificat...
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Ci...