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SIAM Journal on Scientific Computing
Article . 2007 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2006
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Fast Computation of Fourier Integral Operators

Authors: Candès, Emmanuel; Demanet, Laurent; Ying, Lexing;

Fast Computation of Fourier Integral Operators

Abstract

We introduce a general purpose algorithm for rapidly computing certain types of oscillatory integrals which frequently arise in problems connected to wave propagation and general hyperbolic equations. The problem is to evaluate numerically a so-called Fourier integral operator (FIO) of the form $\int e^{2��i ��(x,��)} a(x,��) \hat{f}(��) \mathrm{d}��$ at points given on a Cartesian grid. Here, $��$ is a frequency variable, $\hat f(��)$ is the Fourier transform of the input $f$, $a(x,��)$ is an amplitude and $��(x,��)$ is a phase function, which is typically as large as $|��|$; hence the integral is highly oscillatory at high frequencies. Because an FIO is a dense matrix, a naive matrix vector product with an input given on a Cartesian grid of size $N$ by $N$ would require $O(N^4)$ operations. This paper develops a new numerical algorithm which requires $O(N^{2.5} \log N)$ operations, and as low as $O(\sqrt{N})$ in storage space. It operates by localizing the integral over polar wedges with small angular aperture in the frequency plane. On each wedge, the algorithm factorizes the kernel $e^{2 ��i ��(x,��)} a(x,��)$ into two components: 1) a diffeomorphism which is handled by means of a nonuniform FFT and 2) a residual factor which is handled by numerical separation of the spatial and frequency variables. The key to the complexity and accuracy estimates is that the separation rank of the residual kernel is \emph{provably independent of the problem size}. Several numerical examples demonstrate the efficiency and accuracy of the proposed methodology. We also discuss the potential of our ideas for various applications such as reflection seismology.

31 pages, 3 figures

Keywords

nonuniform fast Fourier transform, 86A15, randomized algorithms, Numerical Analysis (math.NA), 35S30; 65F30; 86A15, 530, matrix approximation, 510, separated representation, reflection seismology, 35S30, Fourier integral operators, FOS: Mathematics, operator compression, Mathematics - Numerical Analysis, generalized Radon transform, 65F30

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    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
71
Top 10%
Top 10%
Top 10%
Green
bronze