2000
DOI: 10.1016/s0097-8485(99)00096-0
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A generalization of Numerov's method for the numerical solution of the Schrödinger equation in two dimensions

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2001
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Cited by 9 publications
(12 citation statements)
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“…But, the Hamiltonian is diagonal, and there are some numerical method to solve it. We use a well-known Numerov numerical methods to solve ordinary differential equations of second order [19], [20]. In this method, we first organize the square matrix Hamiltonian…”
Section: D Potential In Cylindrical Coordinatesmentioning
confidence: 99%
“…But, the Hamiltonian is diagonal, and there are some numerical method to solve it. We use a well-known Numerov numerical methods to solve ordinary differential equations of second order [19], [20]. In this method, we first organize the square matrix Hamiltonian…”
Section: D Potential In Cylindrical Coordinatesmentioning
confidence: 99%
“…Several other variants in order to increase both stability and accuracy while calculating the resonance problems and high−lying bound states were proposed. Generalization of the algorithm to an error of arbitrary order [15][16][17][18][19][20][21][22][23][24][25] and extension it for solution of differential equations with more than one−dimension [8,[26][27][28][29][30][31][32][33] form the main framework of the studies. A Numerov−type exponentially fitted method was suggested [18-20, 32, 34-43], accordingly.…”
Section: Introductionmentioning
confidence: 99%
“…One of the reasons for the 1D case has earned that much attention lies partially in the fact that many higher dimensional problems have spherically symmetric potentials which then allows separation of variables and the study of the radial equation only. Numerical methods for obtaining bound state as well as scattering state solutions can be broadly classified into two types: numerical integration or in other words the shooting methods [3,4] and matrix methods [2,5,6,7,8,9,10]. Also, there are analytical approaches too like perturbative treatments or the Rayleigh-Ritz variational principle, see Ref.…”
Section: Introductionmentioning
confidence: 99%
“…This is because the first approximation already yields very accurate eigenvalues with error at most O(h 4 ). This is also true in two and higher dimensions, though the procedure is slightly complicated by the fact that the recurrence relation involves second-neighbors as well and so some additional information or physically motivated approximation is needed close to the boundary in order to start the algorithm [9,14]. Though its performance, in the sense of accuracy, is obviously not as good as the classical heptadiagonal approximation, with higher order approximations for the derivatives [13,17,18] or with extrapolation techniques [7,8,9] (Richardson, Aitken) it can be refined easily up to O(h 6 ) or higher.…”
Section: Introductionmentioning
confidence: 99%