2017
DOI: 10.1016/j.cpc.2017.07.017
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Efficient block preconditioned eigensolvers for linear response time-dependent density functional theory

Abstract: We present two efficient iterative algorithms for solving the linear response eigenvalue problem arising from the time dependent density functional theory. Although the matrix to be diagonalized is nonsymmetric, it has a special structure that can be exploited to save both memory and floating point operations. In particular, the nonsymmetric eigenvalue problem can be transformed into a product eigenvalue problem that is self-adjoint with respect to a K-inner product. This product eigenvalue problem can be solv… Show more

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Cited by 16 publications
(16 citation statements)
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References 32 publications
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“…A C-neutral vector is a vector v ∈ C 2n which satisfies v H Cnv = 0. 4 A similar behavior has been observed in[36] when solving the linear response eigenvalue problem. 5 http://www.nersc.gov/users/computational-systems/edison/…”
supporting
confidence: 64%
“…A C-neutral vector is a vector v ∈ C 2n which satisfies v H Cnv = 0. 4 A similar behavior has been observed in[36] when solving the linear response eigenvalue problem. 5 http://www.nersc.gov/users/computational-systems/edison/…”
supporting
confidence: 64%
“…Specifically, we adopt the Locally Optimal Block Preconditioned Gradient (LOBPCG) algorithm [17] as our eigensolver. The LOBPCG algorithm and its variants have been used to solve eigenvalue problems arising from a number of applications, including material science [6,15,20,37] and machine learning [18,19,24]. The use of a block iterative method allows us to improve the memory access pattern of the computation and make use of approximations to several eigenvectors at the same time.…”
Section: Introductionmentioning
confidence: 99%
“…The Davidson algorithms 76,77 form a family of subspace-iterative diagonalization schemes that are extensively used in large-scale quantum chemistry applications. 78,79 These methods allow rapid computation of a selected numbers of eigenvalues of large matrices, while reducing memory requirements when compared to other methods.…”
Section: E Iterative Matrix-free Eigensolvers For the Bsementioning
confidence: 99%