2014
DOI: 10.1103/physrevb.90.195109
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Block Lanczos density-matrix renormalization group method for general Anderson impurity models: Application to magnetic impurity problems in graphene

Abstract: We introduce a block Lanczos (BL) recursive technique to construct quasi-one-dimensional models, suitable for density-matrix renormalization group (DMRG) calculations, from single-as well as multiple-impurity Anderson models in any spatial dimensions. This new scheme, named BL-DMRG method, allows us to calculate not only local but also spatially dependent static and dynamical quantities of the ground state for general Anderson impurity models without losing elaborate geometrical information of the lattice. We … Show more

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Cited by 34 publications
(54 citation statements)
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“…39 for an independent development of the same ideas). This approach enables one to study quantum impurity problems with a real space representation of the lattice, and in arbitrary dimensions, using the density matrix renormalization group method (DMRG) 40,41 .…”
Section: Fig 1 (A)mentioning
confidence: 99%
“…39 for an independent development of the same ideas). This approach enables one to study quantum impurity problems with a real space representation of the lattice, and in arbitrary dimensions, using the density matrix renormalization group method (DMRG) 40,41 .…”
Section: Fig 1 (A)mentioning
confidence: 99%
“…In order to make the problem numerically tractable, we employ the so-called block Lanczos method recently introduced in this context by two of the authors 32,45 . This approach is inspired by Wilson's original formulation of the numerical renormalization group 46 , but accounting for the lattice structure.…”
Section: B Numerical Approachmentioning
confidence: 99%
“…We point out that this technique has been described in the literature in great detail 32,45 , so we cover only the essential aspects. Section III describes the exact results obtained with this approach both for single, and bi-layer graphene, illustrating the departure from conventional perturbative predictions.…”
Section: Introductionmentioning
confidence: 99%