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Journal of Scientific Computing

, Volume 77, Issue 1, pp 283–307 | Cite as

A Stable Fast Time-Stepping Method for Fractional Integral and Derivative Operators

  • Fanhai Zeng
  • Ian Turner
  • Kevin Burrage
Article

Abstract

A unified fast time-stepping method for both fractional integral and derivative operators is proposed. The fractional operator is decomposed into a local part with memory length \(\varDelta T\) and a history part, where the local part is approximated by the direct convolution method and the history part is approximated by a fast memory-saving method. The fast method has \(O(n_0+\sum _{\ell }^L{q}_{\alpha }(N_{\ell }))\) active memory and \(O(n_0n_T+ (n_T-n_0)\sum _{\ell }^L{q}_{\alpha }(N_{\ell }))\) operations, where \(L=\log (n_T-n_0)\), \(n_0={\varDelta T}/\tau ,n_T=T/\tau \), \(\tau \) is the stepsize, T is the final time, and \({q}_{\alpha }{(N_{\ell })}\) is the number of quadrature points used in the truncated Laguerre–Gauss (LG) quadrature. The error bound of the present fast method is analyzed. It is shown that the error from the truncated LG quadrature is independent of the stepsize, and can be made arbitrarily small by choosing suitable parameters that are given explicitly. Numerical examples are presented to verify the effectiveness of the current fast method.

Keywords

Fast convolution The (truncated) Laguerre–Gauss quadrature Short memory principle Fractional differential equations Fractional Lorenz system 

Notes

Acknowledgements

The authors are very grateful to the anonymous referees for the careful reading of a preliminary version of the manuscript and their valuable suggestions and comments, which greatly improve the quality of this paper.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematical SciencesQueensland University of TechnologyBrisbaneAustralia
  2. 2.Australian Research Council Centre of Excellence for Mathematical and Statistical FrontiersQueensland University of TechnologyBrisbaneAustralia
  3. 3.Department of Computer ScienceUniversity of OxfordOxfordUK

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