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Laguerre polynomials

Combinatorics

Machine learning

Optics

Physics

Mathematical analysis

Fractional calculus

Repressor

Gaussian quadrature

Operator (biology)

Chemistry

Integral equation

Mathematics

Computer science

Convolution (computer science)

Nyström method

Artificial neural network

Biochemistry

Quadrature (astronomy)

Transcription factor

Journal of Scientific Computing

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.

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