arXiv subjects
Publications and source records attributed to Amna Noreen.
We announce a Python class for numerical solution of Schr{ö}dinger equations in one or more space dimensions, employing some recently developed general classes for numerical solution of partial differential equations, and routines from \texttt{numpy} and \texttt{scipy.sparse.linalg} (or \texttt{scipy.linalg} for smaller problems).
This is the introductory part of my Ph.D thesis, defended at the Faculty of Science and Technology, NTNU on December 10, 2012.
The Frobenius method can be used to compute solutions of ordinary linear differential equations by generalized power series. Each series converges in a circle which at least extends to the nearest singular point; hence exponentially fast inside the circle. This makes this method well suited for very-high-precision solutions of such equations. It is useful for this purpose to have prior knowledge of the behaviour of the series. We show that the magnitude of its coefficients can be apriori predicted to surprisingly high accuracy, employing a Legendre transformation of the WKB approximated solutions of the equation.
We compare predictions of the quantum loop expansion to (essentially) infinite orders with (essentially) exact results in a simple quantum mechanical model.We find that there are exponentially small corrections to the loop expansion, which cannot be explained by any obvious "instanton" type corrections. It is not the mathematical occurence of exponential corrections, but their seemingly lack of any physical origin, which we find surprising and puzzling.
The Frobenius method can be used to represent solutions of ordinary differential equations by (generalized) power series. It is useful to have prior knowledge of the coefficients of this series. In this contribution we demonstrate that the magnitude of the coefficients can be predicted to surprisingly high accuracy by a Legendre transformation of WKB approximated solutions to the differential equations.
A subroutine for very-high-precision numerical solution of a class of ordinary differential equations is provided. For given evaluation point and equation parameters the memory requirement scales linearly with precision $P$, and the number of algebraic operations scales roughly linearly with $P$ when $P$ becomes sufficiently large. We discuss results from extensive tests of the code, and how one for a given evaluation point and equation parameters may estimate precision loss and computing time in advance.
We demonstrate that it is possible to compute wave function normalization constants for a class of Schrödinger type equations by an algorithm which scales linearly (in the number of eigenfunction evaluations) with the desired precision P in decimals.
We investigate a method to solve a class of Schr{ö}dinger equation eigenvalue problems numerically to very high precision $P$ (from thousands to a million of decimals). The memory requirement, and the number of high precision algebraic operations, of the method scale essentially linearly with $P$ when only eigenvalues are computed. However, since the algorithms for multiplying high precision numbers scale at a rate between $P^{1.6}$ and $P\,\log P\,\log\log P$, the time requirement of our method increases somewhat faster than $P^2$.