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Research article

An efficient modified hybrid explicit group iterative method for the time-fractional diffusion equation in two space dimensions

  • Received: 31 July 2021 Accepted: 04 November 2021 Published: 11 November 2021
  • MSC : 35XX, 65N12

  • In this paper, a new modified hybrid explicit group (MHEG) iterative method is presented for the efficient and accurate numerical solution of a time-fractional diffusion equation in two space dimensions. The time fractional derivative is defined in the Caputo sense. In the proposed method, a Laplace transformation is used in the temporal domain, and, for the spatial discretization, a new finite difference scheme based on grouping strategy is considered. The unique solvability, unconditional stability and convergence are thoroughly proved by the matrix analysis method. Comparison of numerical results with analytical and other approximate solutions indicates the viability and efficiency of the proposed algorithm.

    Citation: Fouad Mohammad Salama, Nur Nadiah Abd Hamid, Norhashidah Hj. Mohd Ali, Umair Ali. An efficient modified hybrid explicit group iterative method for the time-fractional diffusion equation in two space dimensions[J]. AIMS Mathematics, 2022, 7(2): 2370-2392. doi: 10.3934/math.2022134

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  • In this paper, a new modified hybrid explicit group (MHEG) iterative method is presented for the efficient and accurate numerical solution of a time-fractional diffusion equation in two space dimensions. The time fractional derivative is defined in the Caputo sense. In the proposed method, a Laplace transformation is used in the temporal domain, and, for the spatial discretization, a new finite difference scheme based on grouping strategy is considered. The unique solvability, unconditional stability and convergence are thoroughly proved by the matrix analysis method. Comparison of numerical results with analytical and other approximate solutions indicates the viability and efficiency of the proposed algorithm.



    In recent years, fractional calculus has gained a great deal of attention from the research community due to its widespread applications in physics, chemistry, fluid mechanics, viscoelasticity, finance, control systems and other areas of science and engineering. The phenomena in the aforesaid fields can be modelled very successfully by means of equations containing fractional derivatives and fractional integrals; and therefore, the investigation of fractional differential equations has become a hot topic for many researchers. Fractional kinetic equations including Fokker-Planck equation, fractional diffusion equation, fractional cable equation and fractional advection-diffusion equation are proved to be powerful instruments for modelling transport dynamics in complex systems such as electrodiffusion of ions in spiny dendrites, transport of proteins molecules, movement of a solution in an aquifer, pollution of underground water, movement under the effect of optical tweezers and more [1,2,3,4,5]. To get more insight into the applications of fractional calculus, the interested reader can refer to the classical books in [6,7], besides the recent books in [8,9].

    In this work, we study the following two-dimensional time-fractional diffusion equation with a non-homogeneous source term:

    {C0Dαtu(x,y,t)=ax2u(x,y,t)x2+ay2u(x,y,t)y2+f(x,y,t),(x,y)Ω,0<tT,(1.1)u(x,y,t)=p(x,y,t),(x,y)Ω,0<tT,(1.2)u(x,y,0)=g(x,y),(x,y)ΩΩ,(1.3)

    where f(x,y,t), p(x,y,t) and g(x,y) are known functions, Ω=(0,L)×(0,L) is a bounded domain in R2, Ω is the boundary, ax>0 and ay>0 are the diffusion coefficients, and α(0<α1) is the anomalous diffusion exponent. The term C0Dαtu(x,y,t) stands for the Caputo fractional derivative of the function u(x,y,t) that is defined as,

    C0Dαtu(x,y,t)={1Γ(1α)t0(tξ)αu(x,y,ξ)ξdξ, 0<α<1,u(x,y,t)t, α=1.

    From the above definition, it can be observed that the fractional diffusion problems (1.1)–(1.3) corresponds to the classical diffusion model as α=1. Fractional diffusion equation is one of the most fundamental equations in the literature, and its capability to model many problems in science and engineering is a well established fact. Generally speaking, it has been widely used for describing random walks and modelling phenomena that are governed by anomalous diffusion in various fields such as porous systems, nuclear magnetic resonance and transport in fractal geometries [10,11,12]. Since most fractional differential equations are difficult to handle analytically, many researchers have resorted to numerical techniques, especially the finite difference method for solving fractional diffusion problems [4,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25]. The complexity of fractional differential equations stems from the presence of non-local fractional derivatives that have the property of global dependence on time or space. This forms the principal obstacle to the development of efficient simulation algorithms in terms of CPU time and memory usage, especially for multi-dimensional problems [26,27,28]. To surmount this issue, techniques including fast Poisson solver [14], parallel implementation [15,29], preconditioning [15] and multigrid method [30] were suggested in the literature. Therefore, the question of how we can remove the fractional derivative from our computations to reduce the complexity and establish efficient solution algorithms is considered a big question arising in the numerical simulations of fractional differential equations. In this regard, Ren et al. [31] have introduced a relatively new approach for solving Caputo-type fractional differential equations. In this approach, a Laplace transform technique is proposed to approximate the fractional differential equation by its corresponding integer-order differential equation, which can be solved with less effort by using a suitable numerical or analytical method (see [32,33]). Recently in [34], a new hybrid standard point (HSP) iterative method based on a combination of the Laplace transform technique and implicit difference scheme has been proposed to solve the time-fractional diffusion problems (1.1)–(1.3). The authors showed that their method produces accurate numerical solutions with an optimal computational complexity of O(N) and optimal memory requirement of O(Ms), where N and Ms are the total number of time levels and spatial unknowns, respectively.

    It is well known that the explicit group iterative methods [35,36,37,38,39] based on finite difference approximations are proposed for the numerical solutions of integer-order differential equations. From one side, explicit group methods are unconditionally stable as the conventional implicit schemes. On the other side, they reduce the number of spatial unknowns taken in the iterative process and thus lead to computationally efficient algorithms. Although the grouping methods have been successfully employed to abroad spectrum of classical differential models, very little work has been reported to deal with fractional differential equations using grouping techniques (see [40,41,42,43]). In addition, the handling of nonlinear and variable order fractional differential equations by explicit group methods, besides their parallel implementation, is another topic that is still at its infancy. Therefore, this subject needs a major development.

    The main purpose of this paper is to combine the Laplace transform technique with an explicit group scheme to solve the two-dimensional time-fractional diffusion Eqs (1.1)–(1.3). The unconditional stability, convergence and solvability of the resulting method, namely the modified hybrid explicit group (MHEG) method are rigorously proved. To illustrate the efficiency and feasibility of the proposed method, the fast HSP iterative method based on the recent work in [34] is also presented.

    The rest of this paper is organized as follows. In Section 2, we briefly review the existing HSP iterative method [34] for solving the problems (1.1)–(1.3). In Section 3, the MHEG iterative method is proposed, and its unconditional stability, convergence and solvability are rigorously proved in Section 4. In Section 5, numerical simulations are performed to indicate the efficiency of the proposed method and support our theoretical analysis. Finally, this work ends with a brief summary in Section 6.

    Since the Caputo fractional derivative in (1.1) is non-local and has the character of history dependence, conventional difference schemes (such as an implicit or explicit scheme with a particular discretiztion formula for the fractional derivative) for the problems (1.1)–(1.3) result in costly simulations in terms of CPU time and memory consumption. In order to surmount the computational challenge, and utilizing the Laplace transform technique, the Caputo's time fractional derivative is approximated as follows [34]:

    C0Dαtu(x,y,t)αu(x,y,t)t+(1α)[u(x,y,t)u(x,y,0)]. (2.1)

    By substituting (2.1) into (1.1), the original time-fractional diffusion Eq (1.1) is reduced to its corresponding integer-order partial differential equation (PDE) with the following initial and boundary conditions [34]:

    {u(x,y,t)t=Ax2u(x,y,t)x2+Ay2u(x,y,t)y2(r1)u(x,y,t)+(r1)g(x,y)+rf(x,y,t),(x,y)Ω,0<tT,(2.2)u(x,y,t)=p(x,y,t),(x,y)Ω,0<tT,(2.3)u(x,y,0)=g(x,y),(x,y)ΩΩ,(2.4)

    where Ax=ax/α, Ay=ay/α and r=1/α are positive constants. Next, we let

    xi=ih, i=0,1,,M,yj=jh, j=0,1,,M,tk=kτ, k=0,1,,N,

    where h=L/M and τ=T/N are the uniform space and time step sizes, respectively. We also introduce the grid functions given by

    uki,j=u(xi,yj,tk),u0i,j=g(xi,yj),fki,j=f(xi,yj,tk),0i,jM, 0kN.

    Here, the notations δtUk+1i,j, δ2xUk+1i,j and δ2yUk+1i,j are defined as follows:

    δtUk+1i,j=1τ(Uk+1i,jUki,j)+O(τ),δ2xUk+1i,j=1h2(Uk+1i+1,j2Uk+1i,j+Uk+1i1,j)+O(h2),δ2yUk+1i,j=1h2(Uk+1i,j+12Uk+1i,j+Uk+1i,j1)+O(h2). (2.5)

    Now, applying the difference operators in (2.5) to the approximating PDEs (2.2)–(2.4) and disregarding the truncation errors, we obtain the implicit difference scheme in the following form:

    {uk+1i,j=1(1+(r1)τ+2q1+2q2)[q1(uk+1i+1,j+uk+1i1,j)+q2(uk+1i,j+1+uk+1i,j1),+uki,j+(r1)τu0i,j+rτfk+1i,j],1i,jM1,0kN1,(2.6)uki,j|Ω=p(xi,yj,tk),0kN,(2.7)u0i,j=g(xi,yj),0i,jM,(2.8)

    where q1=Axτ/h2, q2=Ayτ/h2 and uki,j is the numerical approximation of the function Uki,j after neglecting the truncation errors.

    Considering the fact that the spatial and temporal discretizations usually lead to large and sparse systems of linear equations to be solved, iterative solvers, such as Gauss-Seidel method, are more practical and economic than the direct solvers. Here, and at each time level, the HSP method functions through the iterative evaluation of solutions at all mesh points shown in Figure 1 by using Eq (2.7) until certain convergence is achieved. Once the converged solution values uk+1=(uk+111,uk+11,2,,uk+11,M1,,uk+1M1,1,uk+1M1,2,,uk+1M1,M1)T are attained, they are utilized as an initial guess for the next time level uk+2=(uk+211,uk+21,2,,uk+21,M1,,uk+2M1,1,uk+2M1,2,,uk+2M1,M1)T. Such process is repeated until it reaches the targeted time level. The HSP iterative method has been shown to reduce the memory requirement, computational complexity as well as the CPU time greatly when compared to the fractional standard point (FSP) iterative methods derived based on the conventional implicit difference schemes. For extra details, please refer to [34]. Seeking for more efficient solution algorithm, we propose our method in the next section.

    Figure 1.  Distribution of solution mesh points for the HSP method when M=10.

    Consider the standard mesh of points with the spatial step size 2h=2L/M illustrated in Figure 2. We begin this section by discretizing the derivatives of the space variables in (2.2) around these 2h spaced points. Recalling the δt definition, the spatial difference operators δ2x and δ2y are defined as follows:

    δtUk+1i,j=1τ(Uk+1i,jUki,j)+O(τ),δ2xUk+1i,j=1h2(Uk+1i+2,j2Uk+1i,j+Uk+1i2,j)+O(h2),δ2yUk+1i,j=1h2(Uk+1i,j+22Uk+1i,j+Uk+1i,j2)+O(h2). (3.1)
    Figure 2.  Distribution of solution mesh points for the MHEG method when M=10.

    Applying the difference operators in (3.1) to the approximating PDE (2.2), we obtain

    uk+1i,juki,jτ=Ax(uk+1i+2,j2uk+1i,j+uk+1i2,j4h2)+Ay(uk+1i,j+22uk+1i,j+uk+1i,j24h2)(r1)uk+1i,j+(r1)u0i,j+rfk+1i,j+O(τ+h2). (3.2)

    After simplification and imposing the initial and boundary conditions, the following implicit scheme is attained:

    {uk+1i,j=1(1+(r1)τ+q1/2+q2/2)[q14(uk+1i+2,j+uk+1i2,j)+q24(uk+1i,j+2+uk+1i,j2),+uki,j+(r1)τu0i,j+rτfk+1i,j],2i,jM2,0kN1,(3.3)uki,j|Ω=p(xi,yj,tk),0kN,(3.4)u0i,j=g(xi,yj),0i,jM,(3.5)

    where uki,j is the approximate numerical solution of Uki,j after omitting the truncation error terms. The MHEG method can now be constructed by applying Eq (3.3) to a group of four mesh points of the solution domain. This will result in the following (4×4) system of equations:

    (Dq1/40q2/4q1/4Dq2/400q2/4Dq1/4q2/40q1/4D)(uk+1i,juk+1i+2,juk+1i+2,j+2uk+1i,j+2)=(rhsi,jrhsi+2,jrhsi+2,j+2rhsi,j+2), (3.6)

    where

    D=1+(r1)τ+q1/2+q2/2,rhsi,j=q14uk+1i2,j+q24uk+1i,j2+uki,j+(r1)τu0i,j+rτfk+1i,j,rhsi+2,j=q14uk+1i+4,j+q24uk+1i+2,j2+uki+2,j+(r1)τu0i+2,j+rτfk+1i+2,j,rhsi+2,j+2=q14uk+1i+4,j+2+q24uk+1i+2,j+4+uki+2,j+2+(r1)τu0i+2,j+2+rτfk+1i+2,j+2,rhsi,j+2=q14uk+1i2,j+2+q24uk+1i,j+4+uki,j+2+(r1)τu0i,j+2+rτfk+1i,j+2.

    The coefficients matrix in (3.6) can be inverted to get the MHEG equation given as below:

    (uk+1i,juk+1i+2,juk+1i+2,j+2uk+1i,j+2)=1η(η1η2η3η4η2η1η4η3η3η4η1η2η4η3η2η1)(rhsi,jrhsi+2,jrhsi+2,j+2rhsi,j+2), (3.7)

    where

    η=1256(4+4(r1)τ+q1+q2)(4+4(r1)τ+3q1+q2)(4+4(r1)τ+q1+3q2)×(4+4(r1)τ+3q1+3q2),η1=132(2+2(r1)τ+q1+q2)(16+16q1+16q2+8q1q2+3q21+3q22+16q1(r1)τ+16q2(r1)τ+32(r1)τ+16(r1)2τ2),η2=164q1(16+16q1+16q2+8q1q2+3q21+5q22+16q1(r1)τ+16q2(r1)τ+32(r1)τ+16(r1)2τ2),η3=116q1q2(2+2(r1)τ+q1+q2),η4=164q2(16+16q1+16q2+8q1q2+5q21+3q22+16q1(r1)τ+16q2(r1)τ+32(r1)τ+16(r1)2τ2).

    Back to Figure 2, it can be seen that the discretized solution domain comprises three different types of mesh points (, , ○). Here, the MHEG method functions through the iterative evaluation of solutions at mesh points by utilizing Eq (3.7) until a certain convergence criterion is met. Afterwards, the solution values at the remaining mesh points of types and ○ are computed directly once in a particular sequence. The computational cost of the iterative method still depends on the number of unknowns involved in the iteration process. For the MHEG method, it is obvious that the number of mesh points taken in the iteration process is lesser than that of the prespecified HSP method, which is expected to speed up the convergence rate of the proposed method. For convenience, the MHEG method in combination with the Gauss-Seidel iterative solver is elaborated in Algorithm 1.

    Algorithm 1: MHEG method utilizing the Gauss-Seidel iterative solver
      1: Branch the mesh points of the discretized solution domain into the following categories:
        ● Iterative points of type .
        ● Direct points of types and ○.
      2: Arrange all the points into groups of four mesh points as depicted in Figure 2.
      3: Set an initial guess for the solution at the current time level.
      4: For each four-point group at the current time level, iterate the intermediate solutions at mesh points utilizing
            (ˆuk+1,n+1i,jˆuk+1,n+1i+2,jˆuk+1,n+1i+2,j+2ˆuk+1,n+1i,j+2)=1η(η1η2η3η4η2η1η4η3η3η4η1η2η4η3η2η1)(rhsi,jrhsi+2,jrhsi+2,j+2rhsi,j+2),
      where
          rhsi,j=q14uk+1i2,j+q24uk+1i,j2+uki,j+(r1)τu0i,j+rτfk+1i,j,rhsi+2,j=q14uk+1i+4,j+q24uk+1i+2,j2+uki+2,j+(r1)τu0i+2,j+rτfk+1i+2,j,rhsi+2,j+2=q14uk+1i+4,j+2+q24uk+1i+2,j+4+uki+2,j+2+(r1)τu0i+2,j+2+rτfk+1i+2,j+2,rhsi,j+2=q14uk+1i2,j+2+q24uk+1i,j+4+uki,j+2+(r1)τu0i,j+2+rτfk+1i,j+2,
      and perform the Gauss-Seidel solver
            (uk+1,n+1i,juk+1,n+1i+2,juk+1,n+1i+2,j+2uk+1,n+1i,j+2)=ω(ˆuk+1,n+1i,jˆuk+1,n+1i+2,jˆuk+1,n+1i+2,j+2ˆuk+1,n+1i,j+2)+(1ω)(uk+1,ni,juk+1,ni+2,juk+1,ni+2,j+2uk+1,ni,j+2),
      where n is the iteration number and ω=1 is the relaxation parameter of the Gauss-Seidel solver. η, η1, η2, η3 and η4 are as defined before in this section.
      5: Test the convergence. If convergence is achieved, go to step 6. Otherwise, step 4 is repeated until convergence is attained.
      6: The solutions on the rest of mesh points of types and ○ are evaluated directly once in the following manner:
        ● For points, the approximating PDE (2.2) is discretized by using skewed finite difference operators. Such difference operators are derived by rotating the standard mesh an angle 45 clockwise and applying the Taylor series expansion afterwards [45]. This will lead to the following skewed difference scheme for (2.2):
            uk+1i,j=1(1+(r1)τ+q1+q2)[q12(uk+1i+1,j1+uk+1i1,j+1)+q22(uk+1i+1,j+1+uk+1i1,j1)+uki,j+(r1)τu0i,j+rτfk+1i,j].
        ● For ○ points, the difference scheme formula defined by (2.6) is employed.
      7: If the final time level is reached, go to step 8. Otherwise, adopt the solution values of the last time level as an initial guess for the next time level and go to step 4.
      8: Display the numerical solutions.

     | Show Table
    DownLoad: CSV

    In this section, we turn to consider the stability and convergence of the MHEG scheme defined by (3.7) by using the matrix analysis method. For the convenience of our analysis, three remarks are given at first.

    Remark 4.1. Let zN. The symbol Az×z denotes the infinity norm of matrix A=[ai,j] which is defined as,

    Az×z=max1iz{zj=1|ai,j|}.

    Remark 4.2. Az×z is called a stricatly diagonally dominant (SDD) matrix if |ai,i|>ri(A), where ri(A) is the i-th deleted absolute row sum (i.e., ri(A)=zji,j=1|ai,j|, 1iz).

    Remark 4.3. Given that Az×z is a SDD matrix, then Az×z is non-singular and the following norm's bound does hold [44]:

    A11min1iM{|ai,i|ri(A)}.

    Here, we analyze the stability of the MHEG scheme (3.7). For simplicity and without loss of generality, we assume that q1=q2=q=τ/h2. Indeed, the MHEG scheme (3.7) can be represented in the following matrix form:

    Auk+1=Buk+Cu0+b,0kN1, (4.1)

    where ukis an (M2)24-dimensional vector expressed in the block form

    uk=(uk1,uk2,,uk(M2)216)T, ukl=(uki,j,uki+2,j,uki+2,j+2,uki,j+2)T, 1l(M2)216,

    and

    A=(J1J2J3J1J2J3J1J2J3J1),B=(H1H1H1H1),b=(S1S1S1S1),
    C=(P1P1P1P1),J1=(Q1Q3Q2Q1Q3Q2Q1Q3Q2Q1),J2=(Q5Q5Q5Q5),J3=(Q4Q4Q4Q4),H1=(I4I4I4I4),P1=(T1T1T1T1),S1=(G1G1G1G1),Q1=(1+(r1)τ+qq/40q/4q41+(r1)τ+qq/400q/41+(r1)τ+qq/4q/40q/41+(r1)τ+q),Q2=(000q/400q/4000000000),Q3=(000000000q/400q/4000),Q4=(0q/4000000000000q/40),Q5=(0000q/4000000q/40000),
    T1=((r1)τ0000(r1)τ0000(r1)τ0000(r1)τ),I4=(1000010000100001),G1=rτ(fi,jfi+2,jfi+2,j+2fi,j+2).

    Taking note of the above block matrices, the matrix representation in (4.1) can be rewritten in the more elaborated general form

    [A(M2)24×(M2)24]uk+1=[B(M2)24×(M2)24]uk+[C(M2)24×(M2)24]u0+b. (4.2)

    Next, we prove the following result:

    Theorem 4.1. The MHEG scheme defined by (4.2) is unconditionally stable.

    Proof. Suppose uk and ˜uk are respectively the exact and approximate solutions of (4.2). Then the error at time level k is expressed as ek=uk˜uk. From Remarks 4.2 and 4.3, it follows that A is invertible and therefore Eq (4.2) can be rewritten as

    uk+1=A1Buk+A1Cu0+A1b,0kN1. (4.3)

    The error function ek satisfies (4.3) and leads to the following round-off error equation:

    ek+1=A1Bek+A1Ce0,0kN1, (4.4)

    where

    ek+1=(ek+10ek+10ek+10),ek+10=(ψk+11ψk+12ψk+1(M2)216),ψk+1=(ψk+1i,jψk+1i+2,jψk+1i+2,j+2ψk+1i,j+2),

    and ψk+1i,j=uk+1i,j˜uk+1.

    In order to demonstrate the stability, we shall prove that ek+1e0 for all 0kN1. To this end, the mathematical induction will be used. For k=0, we obtain

    e1=A1Be0+A1Ce0.

    Using the fact that the matrix and the vector infinity norms are consistent, yields

    e1=A1Be0+A1Ce0A1Be0+A1Ce0A1Be0+A1Ce0=(B+C)A1e0.

    Define ri(A) as in Remark 4.2. Then using Remark 4.3, we get

    e1(B+C)min1iM{|ai,i|ri(A)}e01+(r1)τ|1+(r1)τ+q|(|q/4|+|q/4|+|q/4|+|q/4|)=1+(r1)τ1+(r1)τe0=e0.

    Next, suppose that

    es+1e0,s=1,2,,k1. (4.5)

    We will show the above inequality is true for s=k. Noticing (4.4) and (4.5), we get

    ek+1=A1Bek+A1Ce0A1Bek+A1Ce0A1Be0+A1Ce0(B+C)min1iM{|ai,i|ri(A)}e0=1+(r1)τ|1+(r1)τ+q|(|q/4|+|q/4|+|q/4|+|q/4|)=1+(r1)τ1+(r1)τe0=e0.

    Thus, the proof is completed.

    We now analyze the convergence of the MHEG scheme (3.7) by following a similar approach to that in the previous subsection. As a start, we suppose that the truncation errors on each four-point group of mesh points at any time level are represented by the block vector of the following form:

    Rk+1=(Rk+11,Rk+12,,Rk+1(M2)216)T,Rn+1l=(Rk+1i,j,Rk+1i+2,j,Rk+1i+2,j+2,Rk+1i,j+2)T, 1l(M2)216.

    Noticing Eq (3.2) and since i, j and k are finite, there is a positive constant C such that

    Rk+1C(τ+h2),0kN1. (4.6)

    Subtracting (4.2) from the following equation:

    AUk+1=BUk+CU0+b+Rk+1,

    will result in error equation given by

    AEk+1=BEk+CE0+Rk+1, (4.7)

    where

    Ek+1=(Ek+10Ek+10Ek+10),Ek+10=(ϕk+11ϕk+12ϕk+1(M2)216),ϕk+1=(ϕk+1i,jϕk+1i+2,jϕk+1i+2,j+2ϕk+1i,j+2),

    and ϕk+1i,j=Uk+1i,juk+1i,j.

    The convergence property is given in the next theorem.

    Theorem 4.2. Assume problem (1.1) has a smooth solution u(x,y,t). Then, the MHEG scheme defined by (3.7) is convergent and the convergence order is O(τ+h2).

    Proof. We will use mathematical induction for the proof. For k=0 and using that E0=0, we obtain

    E1=A1R1.

    In view of of Remark 4.3 and utilizing (4.6), yields

    E1=A1R1A1R11min1iM{|ai,i|ri(A)}C(τ+h2)=11+(r1)τC(τ+h2)=C0(τ+h2),

    where C0=C/(1+(r1)τ).

    E1C0(τ+h2).

    Now, assume that

    Es+1Cs(τ+h2),s=1,2,,k1. (4.8)

    We prove the above result is true for s=k. Noticing (4.7) and (4.8), we get

    Ek+1=A1BEk+A1Rk+1A1BEk+A1Rk+11min1iM{|ai,i|ri(A)}[Ck1(τ+h2)+C(τ+h2)]=11+(r1)τ(Ck1+C)(τ+h2)=Ck(τ+h2),

    where Ck=Ck1+C as limnτ=0.

    En+1Ck(τ+h2),0nN1.

    Hence, the proof is completed by induction.

    Theorem 4.3 The MHEG scheme defined by (3.7) is uniquely solvable.

    Proof. In view of (4.1), The coefficients matrix A of the MHEG scheme (3.7) is an SDD matrix. Consequently, and based on Remark 4.3, it follows that A is a non-singular matrix. This proves the existence and uniqueness of the solution of the MHEG scheme.

    In this section, we report on numerical simulations for (1.1)–(1.3). In this endeavor, two test problems with known exact solutions are given to demonstrate the accuracy and efficiency of the newly developed MHEG method. For comparison purposes, the fast HSP method proposed recently in [34] is applied to solve the two test problems. All simulations using the both methods are performed in Mathematica 11.3 software and run on a computer with an Intel (R) Core (TM) i7-8550U CPU and 8.00 GB of RAM. In these simulations, the Gauss-Seidel iterative solver with a stopping criterion selected to be 105 are employed to generate the corresponding numerical results. To get more insight into these results, and based on the total number of arithmetic operations to be implemented before and after the convergence process, an analysis on the computational complexity of the MHEG and HSP iterative methods is established and presented in Table 1.

    Table 1.  The computational complexity of the HSP [34] and the MHEG iterative methods (σ=M1).
    Method Per iteration After convergence Total operations
    HSP 13σ2Ite - 13σ2Ite
    MHEG 4.5(σ1)2Ite 3.25(3σ2+2σ1) 4.5(σ1)2Ite+3.25(3σ2+2σ1)

     | Show Table
    DownLoad: CSV

    Test problem 5.1. We consider the following diffusion equation of fractional order:

    C0Dαtu(x,y,t)=2u(x,y,t)x2+2u(x,y,t)y2+(2t2αΓ(3α)+2t2)sin(x)sin(y),

    subject to the boundary and initial conditions

    u(x,y,t)|Ω=t2sin(x)sin(y),u(x,y,0)=0,

    in the spatial domain (0,L)×(0,L)=(0,1)2, T=1, and the exact analytic solution is u(x,y,t)=t2sin(x)sin(y).

    In this test, numerical outputs including maximum absolute error Emax=max1i,jM1|u(xi,yj,tN)uNi,j|, CPU time (in seconds), number of iterations (Ite) as well as total number of arithmetic operations are computed for different values of τ, h and α and listed in Table 2. It can be observed that the MHEG iterative method results in significantly faster simulations without compromising too much of the accuracy when compared to the HSP iterative method. To illustrate this further, Figures 3 and 4 show respectively the graphs of CPU time and total operations of both methods against various mesh sizes. Form these figures, it is apparent that the shape of the computing time plots is consistent with the shape of the total computing effort plots. This indicates that the experimental results are in good agreement with our theoretical analysis. The comparison of the exact and the numerical solutions besides the graphical error representation when τ=h=1/18 and α=0.55 are depicted in Figures 5 and 6, respectively. A comparison of the numerical errors of the test problems for different fractional orders shows that the accuracy of the proposed method is greatest at α=0.55, followed by α=0.75 and α=0.95. It is clear that the numerical solution matches well with the exact solution.

    Table 2.  The numerical results of the MHEG and HSP [34] iterative methods in Test problem 5.1.
    α τ=h Method CPU time (sec) Ite Total operations Emax
    0.55
    1/6 HSP 0.062 27 8,775 1.6737E-03
    MHEG 0.015 2 417 1.7983E-03
    1/10 HSP 0.968 56 58,968 9.5405E-04
    MHEG 0.140 11 4,013 1.0764E-03
    1/14 HSP 3.937 86 188,942 6.0571E-04
    MHEG 0.359 16 12,097 7.6144E-04
    1/18 HSP 10.062 117 439,569 3.8681E-04
    MHEG 0.734 22 28,269 5.7584E-04
    0.75
    1/6 HSP 0.062 26 8,450 2.3916E-03
    MHEG 0.015 2 417 2.4750E-03
    1/10 HSP 0.937 53 55,809 1.4477E-03
    MHEG 0.109 10 3,725 1.5593E-03
    1/14 HSP 3.109 80 175,760 9.9823E-04
    MHEG 0.250 15 11,449 1.1488E-03
    1/18 HSP 8.062 107 401,999 6.9546E-04
    MHEG 0.640 21 27,117 9.0926E-04
    0.95
    1/6 HSP 0.062 25 8,125 2.9204E-03
    MHEG 0.015 2 417 2.9789E-03
    1/10 HSP 0.656 50 52,650 1.7502E-03
    MHEG 0.062 10 3,725 1.8611E-03
    1/14 HSP 2.562 75 164,775 1.1935E-03
    MHEG 0.203 14 10,801 1.3410E-03
    1/18 HSP 7.937 100 375,700 8.2842E-04
    MHEG 0.515 19 24,813 1.0461E-03

     | Show Table
    DownLoad: CSV
    Figure 3.  Graphs of CPU times for Test problem 5.1.
    Figure 4.  Graphs of Computing efforts for Test problem 5.1.
    Figure 5.  The exact solution and the numerical solution for Test problem 5.1 when T=1, τ=h=1/18 and α=0.55.
    Figure 6.  The maximum absolute errors of the MHEG method for the Test problem 5.1 at T=1, τ=h=1/18 and α=0.55.

    Test problem 5.2. We consider another diffusion equation of fractional order as follows:

    C0Dαtu(x,y,t)=2u(x,y,t)x2+2u(x,y,t)y2+(2t2αΓ(3α)2t2)e(x+y),

    with the boundary and initial conditions

    u(x,y,t)|Ω=t2e(x+y),u(x,y,0)=0,

    in the spatial domain Ω=(0,1)2, T=1, and the exact analytic solution u(x,y,t)=t2e(x+y).

    The numerical results of solving this problem for various values of τ, h and α are presented in Table 3. Figure 7 highlights the CPU time plots versus several mesh sizes. In view of Table 3 and Figure 7, it can be seen that the MHEG iterative method reduces the CPU time greatly without jeopardizing the accuracy of numerical solutions compared to the HSP iterative method. This can be attributed to the reduction in the number of executed arithmetic operations that leads to a lower computational complexity as shown in Figure 8. Figure 9 introduces the comparison between the exact and the numerical solutions, whereas Figure 10 presents the 3D plot of the maximum errors when τ=h=1/30 and α=0.75. Again, these demonstrate the effectiveness of the proposed MHEG method. Table 4 illustrates the experimental convergence order in the temporal direction using the formula ρ(τ,h)=log2(Emax(2τ,h)/Emax(τ,h)). It can be observed that the performance is in good agreement with the theoretical analysis.

    Table 3.  The numerical results of the MHEG and HSP [34] iterative methods in Test problem 5.2.
    α τ=h Method CPU time (sec) Ite Total operations Emax
    0.55
    1/18 HSP 12.671 170 638,690 6.3352E-03
    MHEG 0.859 29 36,333 6.8017E-03
    1/22 HSP 31.906 221 1,266,993 5.0040E-03
    MHEG 2.359 38 72,833 5.5331E-03
    1/26 HSP 64.234 271 2,201,875 4.0351E-03
    MHEG 3.390 46 125,485 4.6340E-03
    1/30 HSP 118.625 320 3,498,560 3.2629E-03
    MHEG 7.937 55 202,425 3.8992E-03
    0.75
    1/18 HSP 12.640 156 586,092 1.0239E-02
    MHEG 0.625 27 34,029 1.0665E-02
    1/22 HSP 28.828 199 1,140,867 8.4919E-03
    MHEG 1.343 34 65,633 9.0001E-03
    1/26 HSP 57.593 242 1,966,250 7.1948E-03
    MHEG 3.359 42 115,117 7.8080E-03
    1/30 HSP 102.688 284 3,104,972 6.1807E-03
    MHEG 7.328 49 181,257 6.9414E-03
    0.95
    1/18 HSP 12.359 143 537,251 1.1950E-02
    MHEG 0.562 25 31,725 1.2403E-02
    1/22 HSP 25.968 182 1,043,406 9.7841E-03
    MHEG 1.328 31 60,233 1.0285E-02
    1/26 HSP 51.796 219 1,779,375 8.1651E-03
    MHEG 3.203 38 104,749 8.8048E-03
    1/30 HSP 94.796 255 2,787,915 6.9147E-03
    MHEG 6.890 44 163,617 7.6882E-03

     | Show Table
    DownLoad: CSV
    Figure 7.  Graphs of CPU times for Test problem 5.2.
    Figure 8.  Graphs of Computing efforts for Test problem 5.2.
    Figure 9.  The exact solution and the numerical solution for Test problem 5.2 when T=1, τ=h=1/30 and α=0.75.
    Figure 10.  The maximum absolute errors of the MHEG method for the Test problem 5.2 at T=1, τ=h=1/30 and α=0.75.
    Table 4.  Numerical error and computational order with α=0.55 and h=1/22.
    Test problem τ Emax Computational order
    1 1/10 9.9154E-04 0.9835
    1/20 5.0149E-04
    2 1/10 1.1829E-02 0.9658
    1/20 6.0570E-03

     | Show Table
    DownLoad: CSV

    In this paper, a new MHEG iterative method for solving the two-dimensional diffusion equation with time fractional derivative has been developed. The unique solvability, unconditional stability and convergence are proved by the matrix analysis method. Moreover, the feasibility and efficiency of the proposed numerical method are confirmed through the computational outputs drawn from the conducted numerical simulations. The advantages of being uncomplicated, easy to implement and diminishing the amount of computational complexity indicate that the proposed method and numerical analysis in this article can be extended to solve other types of non-linear and variable order fractional differential equations. In addition, the parallel implementation of the proposed MHEG method is an interesting line to study which can be considered as a part of future work.

    This research was supported by Ministry of Higher Education Malaysia, Fundamental Research Grant Scheme with Project Code: FRGS/1/2019/STG06/USM/01/1. We would like to express our gratitude to the anonymous reviewers for their valuable comments.

    The authors have no conflict of interest.



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