Abstract

A new algorithm is presented which provides a fast method for the computation of recently developed Fourier continuations (a particular type of Fourier extension method) that yield superalgebraically convergent Fourier series approximations of nonperiodic functions. Previously, the coefficients of an approximating Fourier series have been obtained by means of a regularized singular value decomposition (SVD)-based least-squares solution to an overdetermined linear system of equations. These SVD methods are effective when the size of the system does not become too large, but they quickly become unwieldy as the number of unknowns in the system grows. We demonstrate a novel decoupling of the least-squares problem which results in two systems of equations, one of which may be solved quickly by means of fast Fourier transforms (FFTs) and another that is demonstrated to be well approximated by a low-rank system. Utilizing randomized algorithms, the low-rank system is reduced to a significantly smaller system of equations. This new system is then efficiently solved with drastically reduced computational cost and memory requirements while still benefiting from the advantages of using a regularized SVD. The computational cost of the new algorithm in on the order of the cost of a single FFT multiplied by a slowly increasing factor that grows only logarithmically with the size of the system.

MSC codes

  1. 42A10
  2. 65T40
  3. 65T50

Keywords

  1. Fourier series
  2. continuation method
  3. Fourier extension
  4. randomized algorithm

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Published In

cover image SIAM Journal on Scientific Computing
SIAM Journal on Scientific Computing
Pages: 3241 - 3260
ISSN (online): 1095-7197

History

Accepted: 25 August 2011
Published online: 10 November 2011

MSC codes

  1. 42A10
  2. 65T40
  3. 65T50

Keywords

  1. Fourier series
  2. continuation method
  3. Fourier extension
  4. randomized algorithm

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