2001
DOI: 10.1088/0305-4470/34/16/103
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(De)localization in the prime Schrödinger operator

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Cited by 8 publications
(12 citation statements)
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“…Note that the non-stationarity can be satisfied for α ≥ 1. In addition, the LDT with mobility edges, has been found numerically for the onedimensional tight-binding model with the FFM model as the potential strength W decreases [16,17,18,19,20,21,22].…”
Section: Introductionmentioning
confidence: 93%
See 1 more Smart Citation
“…Note that the non-stationarity can be satisfied for α ≥ 1. In addition, the LDT with mobility edges, has been found numerically for the onedimensional tight-binding model with the FFM model as the potential strength W decreases [16,17,18,19,20,21,22].…”
Section: Introductionmentioning
confidence: 93%
“…The N −dependence of Λ N with adequate numerical accuracy for the larger system size is necessary to perform the finitesize scaling analysis around the critical point. ∼ 2 17 . The averaging is taken over 2 12 realizations.…”
Section: Strong Disorder Regimementioning
confidence: 99%
“…Bound (9) immediately follows. To prove part (b), one just takes A(N ) = A for every N. Since (B 1 (T )) (A (N (T )) = (A) > 0, the result follows from bound (9).…”
Section: A Quantum Dynamical Lower Bound Derived From Power-law Transmentioning
confidence: 99%
“…As in the case = ( √ 5−1)/2 and = 0, studied in [8], it is possible to improve this lower bound somewhat by exhibiting a suitable set A(N ) (stable under perturbation), studying its Lebesgue measure, and applying (9). The set A(N ) will again be given by the spectra of suitable periodic approximants, and the Lebesgue measure can again be bounded through a fine analysis of the trace map, akin to what is done in [8,19,27]; compare also [25].…”
Section: Sturmian Potentialsmentioning
confidence: 99%
“…E.g., among the latter examples the following one should be mentioned. There is a study of delocalization in binary "0" and "1" system and the sparse potential which takes different values for prime sites only [27]. It is in such a case that the very "sparse" model should also naturally be "nonstationary".…”
Section: Introductionmentioning
confidence: 99%