Matrices arising in linear-response time-dependent density functional theory and many-body perturbation theory, in particular in the Bethe-Salpeter approach, show a 2 × 2 block structure. The motivation to devise new algorithms, instead of using general purpose eigenvalue solvers, comes from the need to solve large problems on high performance computers. This requires parallelizable and communication-avoiding algorithms and implementations. We point out various novel directions for diagonalizing structured matrices. These include the solution of skew-symmetric eigenvalue problems in ELPA, as well as structure preserving spectral divide-and-conquer schemes employing generalized polar decompostions
Abstract. Structured real canonical forms for matrices in Rn×n that are symmetric or skew-symmetric ...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental ma...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental m...
Obtaining the eigenvalues and eigenvectors of large matrices is a key problem in electronic structur...
Obtaining the eigenvalues and eigenvectors of large matrices is a key problem in electronic structur...
This PhD thesis is an important development in the theories, methods, and applications of eigenvalue...
The Bethe-Salpeter eigenvalue problem is a dense structured eigenvalue problem arising from discreti...
Determing excited states in quantum physics or calculating the number of valence electrons in the De...
We present two efficient iterative algorithms for solving the linear response eigenvalue problem ari...
Efficient and accurate methods for computing the density matrix are necessary to be able to perform ...
The Large-Scale Matrix Diagonalization Methods in Chemistry theory institute brought together 41 com...
We present an algorithm to reduce the computational complexity for plane-wave codes used in electron...
Conventional algorithms for the (symmetric or non-symmetric) eigenvalue decomposition and the singul...
<p>The Davidson method has been highly successful for solving for eigenpairs of the large matrices t...
Ab-initio methods for calculating electronic structure represent an important field of material phys...
Abstract. Structured real canonical forms for matrices in Rn×n that are symmetric or skew-symmetric ...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental ma...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental m...
Obtaining the eigenvalues and eigenvectors of large matrices is a key problem in electronic structur...
Obtaining the eigenvalues and eigenvectors of large matrices is a key problem in electronic structur...
This PhD thesis is an important development in the theories, methods, and applications of eigenvalue...
The Bethe-Salpeter eigenvalue problem is a dense structured eigenvalue problem arising from discreti...
Determing excited states in quantum physics or calculating the number of valence electrons in the De...
We present two efficient iterative algorithms for solving the linear response eigenvalue problem ari...
Efficient and accurate methods for computing the density matrix are necessary to be able to perform ...
The Large-Scale Matrix Diagonalization Methods in Chemistry theory institute brought together 41 com...
We present an algorithm to reduce the computational complexity for plane-wave codes used in electron...
Conventional algorithms for the (symmetric or non-symmetric) eigenvalue decomposition and the singul...
<p>The Davidson method has been highly successful for solving for eigenpairs of the large matrices t...
Ab-initio methods for calculating electronic structure represent an important field of material phys...
Abstract. Structured real canonical forms for matrices in Rn×n that are symmetric or skew-symmetric ...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental ma...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental m...