Abstract
We present a survey of fractional differential equations and in particular of the computational cost for their numerical solutions from the view of computer science. The computational complexities of time fractional, space fractional, and space-time fractional equations areO(N2M),O(NM2), andO(NM(M+N)) compared withO(MN) for the classical partial differential equations with finite difference methods, whereM,Nare the number of space grid points and time steps. The potential solutions for this challenge include, but are not limited to, parallel computing, memory access optimization (fractional precomputing operator), short memory principle, fast Fourier transform (FFT) based solutions, alternating direction implicit method, multigrid method, and preconditioner technology. The relationships of these solutions for both space fractional derivative and time fractional derivative are discussed. The authors pointed out that the technologies of parallel computing should be regarded as a basic method to overcome this challenge, and some attention should be paid to the fractional killer applications, high performance iteration methods, high order schemes, and Monte Carlo methods. Since the computation of fractional equations with high dimension and variable order is even heavier, the researchers from the area of mathematics and computer science have opportunity to invent cornerstones in the area of fractional calculus.
Highlights
We present a survey of fractional differential equations and in particular of the computational cost for their numerical solutions from the view of computer science
Fractional differential equations (FDEs) may be divided into two fundamental types: time fractional differential equations and space fractional differential equations
Liu et al [63] developed an implicit radial basis function (RBF) meshless approach for time fractional diffusion equations and found that the presented meshless formulation is very effective for modeling and simulation of fractional differential equations
Summary
There are many numerical solutions proposed for fractional equations [18], such as finite difference method (FDM) [18]. FDM is intuitive to understand and easy to learn for inexperienced researcher from the areas rather than mathematics This survey focuses on FDM for fractional equations. For the numerical solutions of different differential equations, the area of mathematics pays much attention to approximating the equation more accurately and faster (accuracy and speed). The fractional problems with high dimension, long time iterations, and huge grid points will need to be solved. These problems are real challenge for today’s computer technologies and algorithms
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