2019
DOI: 10.1002/jcc.26046
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Efficient representation of the linear density‐density response function

Abstract: We present a thorough derivation of the mathematical foundations of the representation of the molecular linear electronic density-density response function in terms of a computationally highly efficient moment expansion. Our new representation avoids the necessities of computing and storing numerous eigenfunctions of the response kernel by means of a considerable dimensionality reduction about from 10 3 to 10 1 . As the scheme is applicable to any compact, self-adjoint, and positive definite linear operator, w… Show more

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Cited by 4 publications
(25 citation statements)
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“…In this section, we recall the specific notation for the expression of linear operators that we recently introduced. [50] The derivation closely follows the previous version, but differs in the final expression because the basis functions are now allowed to be non-orthogonal for the image as well as the domain of the linear operator.…”
Section: Representation Of Linear Operators Within Non-orthogonal Bmentioning
confidence: 99%
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“…In this section, we recall the specific notation for the expression of linear operators that we recently introduced. [50] The derivation closely follows the previous version, but differs in the final expression because the basis functions are now allowed to be non-orthogonal for the image as well as the domain of the linear operator.…”
Section: Representation Of Linear Operators Within Non-orthogonal Bmentioning
confidence: 99%
“…We recently generalized the moment expansion to any compact, self-adjoint, and positive-definite linear operatorT : [50] Assuming a finite (orthonormal) basis {| P 1 i, | P 2 i, …, | P N i} for a subspace of the domain ofT, we showed recently that there is a specific basis set {| ξ 1 i, | ξ 2 i, …, | ξ N i} for the (finite) image ofT which fulfills a partial orthogonality condition with respect to the domain basis:…”
Section: Derivation Of the Reduced Eigensystem Representationmentioning
confidence: 99%
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