5,944 research outputs found
A fast analysis-based discrete Hankel transform using asymptotic expansions
A fast and numerically stable algorithm is described for computing the
discrete Hankel transform of order as well as evaluating Schl\"{o}milch and
Fourier--Bessel expansions in
operations. The algorithm is based on an asymptotic expansion for Bessel
functions of large arguments, the fast Fourier transform, and the Neumann
addition formula. All the algorithmic parameters are selected from error bounds
to achieve a near-optimal computational cost for any accuracy goal. Numerical
results demonstrate the efficiency of the resulting algorithm.Comment: 22 page
Fast algorithm for border bases of Artinian Gorenstein algebras
Given a multi-index sequence , we present a new efficient algorithm
to compute generators of the linear recurrence relations between the terms of
. We transform this problem into an algebraic one, by identifying
multi-index sequences, multivariate formal power series and linear functionals
on the ring of multivariate polynomials. In this setting, the recurrence
relations are the elements of the kerne l\sigma of the Hankel operator
$H$\sigma associated to . We describe the correspondence between
multi-index sequences with a Hankel operator of finite rank and Artinian
Gorenstein Algebras. We show how the algebraic structure of the Artinian
Gorenstein algebra \sigma\sigma yields the
structure of the terms $\sigma\alpha N nAK[x 1 ,. .. , xnIHIA$ and the tables of multiplication by the variables in these
bases. It is an extension of Berlekamp-Massey-Sakata (BMS) algorithm, with
improved complexity bounds. We present applications of the method to different
problems such as the decomposition of functions into weighted sums of
exponential functions, sparse interpolation, fast decoding of algebraic codes,
computing the vanishing ideal of points, and tensor decomposition. Some
benchmarks illustrate the practical behavior of the algorithm
Fast multi-dimensional scattered data approximation with Neumann boundary conditions
An important problem in applications is the approximation of a function
from a finite set of randomly scattered data . A common and powerful
approach is to construct a trigonometric least squares approximation based on
the set of exponentials . This leads to fast numerical
algorithms, but suffers from disturbing boundary effects due to the underlying
periodicity assumption on the data, an assumption that is rarely satisfied in
practice. To overcome this drawback we impose Neumann boundary conditions on
the data. This implies the use of cosine polynomials as basis
functions. We show that scattered data approximation using cosine polynomials
leads to a least squares problem involving certain Toeplitz+Hankel matrices. We
derive estimates on the condition number of these matrices. Unlike other
Toeplitz+Hankel matrices, the Toeplitz+Hankel matrices arising in our context
cannot be diagonalized by the discrete cosine transform, but they still allow a
fast matrix-vector multiplication via DCT which gives rise to fast conjugate
gradient type algorithms. We show how the results can be generalized to higher
dimensions. Finally we demonstrate the performance of the proposed method by
applying it to a two-dimensional geophysical scattered data problem
Computation- and Space-Efficient Implementation of SSA
The computational complexity of different steps of the basic SSA is
discussed. It is shown that the use of the general-purpose "blackbox" routines
(e.g. found in packages like LAPACK) leads to huge waste of time resources
since the special Hankel structure of the trajectory matrix is not taken into
account. We outline several state-of-the-art algorithms (for example,
Lanczos-based truncated SVD) which can be modified to exploit the structure of
the trajectory matrix. The key components here are hankel matrix-vector
multiplication and hankelization operator. We show that both can be computed
efficiently by the means of Fast Fourier Transform. The use of these methods
yields the reduction of the worst-case computational complexity from O(N^3) to
O(k N log(N)), where N is series length and k is the number of eigentriples
desired.Comment: 27 pages, 8 figure
About Calculation of the Hankel Transform Using Preliminary Wavelet Transform
The purpose of this paper is to present an algorithm for evaluating Hankel
transform of the null and the first kind. The result is the exact analytical
representation as the series of the Bessel and Struve functions multiplied by
the wavelet coefficients of the input function. Numerical evaluation of the
test function with known analytical Hankel transform illustrates the proposed
algorithm.Comment: 5 pages, 2 figures. Some misprints are correcte
Bogoliubov modes of a dipolar condensate in a cylindrical trap
The calculation of properties of Bose-Einstein condensates with dipolar
interactions has proven a computationally intensive problem due to the long
range nature of the interactions, limiting the scope of applications. In
particular, the lowest lying Bogoliubov excitations in three dimensional
harmonic trap with cylindrical symmetry were so far computed in an indirect
way, by Fourier analysis of time dependent perturbations, or by approximate
variational methods. We have developed a very fast and accurate numerical
algorithm based on the Hankel transform for calculating properties of dipolar
Bose-Einstein condensates in cylindrically symmetric traps. As an application,
we are able to compute many excitation modes by directly solving the
Bogoliubov-De Gennes equations. We explore the behavior of the excited modes in
different trap geometries. We use these results to calculate the quantum
depletion of the condensate by a combination of a computation of the exact
modes and the use of a local density approximation
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