# Performance Numerical Method Half-Sweep Preconditioned Gauss-Seidel for Solving Fractional Diffusion Equation

Performance Numerical Method Half-Sweep Preconditioned Gauss-Seidel for Solving Fractional Diffusion Equation

Andang SunartoJumat Sulaiman

IAIN Bengkulu, Jln Raden Fatah, Pagar Dewa Kota Bengkulu, Indonesia

UMS Malaysia, Jl UMS Kota Kinabalu, Sabah, Malaysia

Corresponding Author Email:
andang99@gmail.com
Page:
201-204
|
DOI:
https://doi.org/10.18280/mmep.070205
17 January 2020
|
Accepted:
28 March 2020
|
Published:
30 June 2020
| Citation

OPEN ACCESS

Abstract:

The main purpose, we derive a finite difference approximation equation from the discretization of the one-dimensional linear space-fractional diffusion equations by using the space fractional derivative of Caputo’s. The linear system will be generated by the Caputo’s finite difference approximation equation. The resulting linear system was then resolved using Half-Sweep Preconditioned Gauss-Seidel (HSPGS) iterative method, which compares its effectiveness with the existing Preconditioned Gauss-Seidel (PGS) or call named (Full-Sweep Preconditioned Gauss-Seidel (FSPGS)) and Gauss-Seidel (HSPGS) methods. Two examples of the issue are provided in order to check the performance efficacy of the proposed approach. The findings of this study show that the proposed iterative method is superior to FSPGS and GS.

Keywords:

HSPGS, space-fractional, Caputo’s, implicit finite difference

1. Introduction

From The previous studies in [1-5] many successful mathematical models, which are based on fractional partial derivative equations (FPDEs), have been developed. Following to that, there are several methods used to solve these models. For instance, we have transform method [6], which is used to obtain analytical and/or numerical solutions of the fractional diffusion equations (FDE). Other than this method, other researchers have proposed finite difference methods such as explicit, implicit and fast method [7-9] Also it is pointed out that the explicit methods are conditionally stable. Therefore, we discretize the space-fractional diffusion equation via the implicit finite difference discretization scheme and Caputo’s fractional partial derivative of order β in order to derive a Caputo’s implicit finite difference approximation equation.

This approximation equation leads a tridiagonal linear system. Due to the properties of the coefficient matrix of the linear system which is sparse and large scale, iterative methods are the alternative option for efficient solutions. Among the existing iterative methods, the preconditioned iterative methods [10-12] have been widely accepted to be one of the efficient methods for solving linear systems.

Because of the advantages of these iterative methods, the aim of this paper is to construct and investigate the performance effectiveness of the Half-Sweep Preconditioned Gauss-Seidel (HSPGS) iterative method for solving space-fractional parabolic partial differential equations (SPPDE’s) based on the Caputo’s implicit finite difference approximation equation. To investigate the effectiveness of the HSPGS method, we also implement the Gauss Seidel (GS) and FSPGS iterative methods being used a control method.

To performance the effectiveness of HSPGS method, let space-fractional parabolic partial differential equation (SPPDE’s) be defined as:

$\text{ }\frac{\partial \text{U}\left( \text{x,t} \right)}{\partial \text{t}}=\text{a}\left( \text{x} \right)\frac{{{\partial }^{\beta }}\text{U}\left( \text{x,t} \right)}{\partial {{\text{x}}^{\beta }}}+\text{b}\left( \text{x} \right)\frac{\partial \text{U}\left( \text{x,t} \right)}{\partial \text{x}}+\text{c}\left( \text{x} \right)\text{U}\left( \text{x,t} \right)+\text{f}\left( \text{x,t} \right)$        (1)

with initial condition $\mathrm{U}(\mathrm{x}, 0)=\mathrm{f}(\mathrm{x}), 0 \leq \mathrm{x} \leq \ell,$ and boundary conditions $\mathrm{U}(0, \mathrm{t})=\mathrm{g}_{0}(\mathrm{t}), 0<\mathrm{t} \leq \mathrm{T}, \mathrm{U}(\ell, \mathrm{t})=\mathrm{g}_{1}(\mathrm{t}), 0<\mathrm{t} \leq \mathrm{T}$

Then to develop the linear systems, some definitions that can be applied for fractional derivative theory need to developing the approximation equation of Eq. (1) in:

Definition 1. [13] The Riemann-Liouville fractional integral operator, $\mathrm{J}^{\beta}$ of order- $\beta$ is defined as:

$\mathrm{J}^{\beta} \mathrm{f}(\mathrm{x})=\frac{1}{\Gamma(\beta)} \int_{0}^{\mathrm{x}}(\mathrm{x}-\mathrm{t})^{\beta-1} \mathrm{f}(\mathrm{t}) \mathrm{d} \mathrm{t}, \beta>0, \mathrm{x}>0$     (2)

Definition 2. [13] The Caputo's fractional partial derivative operator, $\mathrm{D}^{\beta}$ of order $-\beta$ is defined as:

$\mathrm{D}^{\beta} \mathrm{f}(\mathrm{x})=\frac{1}{\Gamma(\mathrm{m}-\beta)} \int_{0}^{\mathrm{x}} \frac{\mathrm{f}^{(\mathrm{m})}(\mathrm{t})}{(\mathrm{x}-\mathrm{t})^{\beta-\mathrm{m}+1}} \mathrm{d} \mathrm{t}, \quad \beta>0$     (3)

with $\mathrm{m}-1<\beta \leq \mathrm{m}, \mathrm{m} \in \mathrm{N}, \mathrm{x}>0$.

We have the following properties when $m-1<\beta \leq m$, $x>0: \mathrm{D}_{\mathrm{k}}^{\beta}=0,(\mathrm{k} \text {is a constant})$,

$\mathrm{D}^{\beta} \mathrm{x}^{\mathrm{n}}=\left\{\begin{array}{cl}0, & \text { for } \mathrm{n} \in \mathrm{N}_{0} \text { and } \mathrm{n}<[\beta] \\ \frac{\Gamma(\mathrm{n}+1)}{\Gamma(\mathrm{n}+1-\beta)} \mathrm{x}^{\mathrm{n}-\beta}, & \text { for } \mathrm{n} \in \mathrm{N}_{0} \text { and } \mathrm{n} \geq[\beta]\end{array}\right.$

where, function $[\beta]$ denotes the smallest integer greater than or equal to $\beta, \mathrm{N}_{0}=\{0,1,2, \ldots\}$ and $\Gamma(.)$ is the gamma function.

2. Caputo Approximation Derivative

Assume that $h=\frac{\ell}{k}, \mathrm{k}$ is positive integer and using second order approximation, we get

$\frac{{{\partial }^{\beta }}\text{U}\left( {{\text{x}}_{\text{i}}},{{\text{t}}_{\text{n}}} \right)}{\partial {{\text{x}}^{\beta }}}=\frac{1}{\Gamma (2-\beta )}\int\limits_{\text{0}}^{{{\text{t}}_{\text{n}}}}{\frac{{{\partial }^{2}}\text{U}\left( {{\text{x}}_{\text{i}}}\text{,s} \right)}{\partial {{\text{x}}^{\text{2}}}}{{\left( {{\text{t}}_{\text{n}}}-\text{s} \right)}^{1-\beta }}\partial \text{s}}$$=\frac{1}{\Gamma \left( 2-\beta \right)}\sum\limits_{\text{j}=\text{0,2,4}..}^{\text{i-2}}{\int\limits_{\text{jh}}^{\left( \text{j}+\text{1} \right)\text{h}}{\left( \frac{{{\text{U}}_{\text{i-j}+\text{2,n}}}-2{{\text{U}}_{\text{i-j,n}}}+{{\text{U}}_{\text{i-j-2,n}}}}{\text{2}{{\text{h}}^{\text{2}}}} \right)}{{\left( \text{nh-s} \right)}^{\beta }}\partial \text{s}} (4) Let us define \sigma_{\beta, \mathrm{h}}=\frac{2 \mathrm{h}^{-\beta}}{\Gamma(3-\beta)} and \mathrm{g}_{\mathrm{j}}^{\beta}=\left(\frac{\mathrm{j}}{2}+1\right)^{2-\beta}-\frac{\mathrm{j}^{2-\beta}}{2} then the discrete approximation of Eq. (4). \frac{{{\partial }^{\beta }}\text{U}\left( {{\text{x}}_{\text{i}}},{{\text{t}}_{\text{n}}} \right)}{\partial {{\text{x}}^{\beta }}}={{\sigma }_{\beta ,2\text{h}}}\sum\limits_{\text{j}=\text{0,2,4}..}^{\text{i-2}}{{{\text{g}}_{\text{j}}}^{\beta }\left( {{\text{U}}_{\text{i-j}+\text{2,n}}}-2{{\text{U}}_{\text{i-j,n}}}+{{\text{U}}_{\text{i-j-2,n}}} \right)} Now we approximate Eq. (1) by using Caputo’s implicit finite difference approximation: \lambda \left( {{\text{U}}_{\text{i,n}}}-{{\text{U}}_{\text{i,n-2}}} \right)={{\text{a}}_{\text{i}}}{{\sigma }_{\beta ,\text{h}}}\sum\limits_{\text{j}=\text{0}}^{\text{i}-1}{\text{g}_{\text{j}}^{\beta }\left( {{\text{U}}_{\text{i-j}+\text{2,n}}}-2{{\text{U}}_{\text{i-j,n}}}+{{\text{U}}_{\text{i-j-2,n}}} \right)}$$+{{\text{b}}_{\text{i}}}\frac{\left( {{\text{U}}_{\text{i}+\text{2,n}}}-{{\text{U}}_{\text{i-2,n}}} \right)}{4\text{h}}+{{\text{C}}_{\text{i}}}{{\text{U}}_{\text{i,n}}}+{{\text{f}}_{\text{i,n}}}$

for i=2,4,…,m-2. Then we can simplify the scheme approximation equation as:

$\lambda {{\text{U}}_{\text{i,n-2}}}=-{{\text{a}}_{\text{i}}}{{\sigma }_{\beta ,2\text{h}}}\sum\limits_{\text{j}=\text{0,2,4}\text{.}}^{\text{i}-2}{\text{g}_{\text{j}}^{\beta }\left( {{\text{U}}_{\text{i-j}+\text{2,n}}}-2{{\text{U}}_{\text{i-j,n}}}+{{\text{U}}_{\text{i-j-2,n}}} \right)}$ $-\frac{{{\text{b}}_{\text{i}}}}{\text{4h}}\left( {{\text{U}}_{\text{i}+\text{2,n}}}-{{\text{U}}_{\text{i-2,n}}} \right)-{{\text{C}}_{\text{i}}}{{\text{U}}_{\text{i,n}}}+\lambda {{\text{U}}_{\text{i,n}}}-{{\text{f}}_{\text{i,n}}}$

So, we get:

$\therefore \text{b}_{\text{i}}^{\text{*}}{{\text{U}}_{\text{i-2,n}}}+\left( \lambda -\text{c}_{\text{i}}^{\text{*}} \right){{\text{U}}_{\text{i,n}}}-\text{b}_{\text{i}}^{\text{*}}{{\text{U}}_{\text{i}+\text{2,n}}}$ $-\text{a}_{\text{i}}^{\text{*}}\sum\limits_{\text{j}=\text{0,2,4}}^{\text{i}-2}{\text{g}_{\text{j}}^{\beta }\left( {{\text{U}}_{\text{i-j}+\text{2,n}}}-2{{\text{U}}_{\text{i-j,n}}}+{{\text{U}}_{\text{i-j}-\text{2,n}}} \right)}={{\text{f}}_{\text{i}}}$   (5)

where, $a_{i}^{*}=a_{i} \sigma_{\beta, 2 h}, \quad b_{i}^{*}=\frac{b_{i}}{4 h}, c_{i}^{*}=c_{i}, F_{i}^{*}=f_{i, n}$ and $f_{i}=\lambda\left(U_{i, n-2}\right)+F_{i}^{*}$

For simplicity, let Eq.(5) for $n>3$ be rewritten as:

$-{{\text{R}}_{\text{i}}}+{{\alpha }_{i}}{{\text{U}}_{\text{i-6,n}}}+{{\text{s}}_{\text{i}}}{{\text{U}}_{\text{i-4}}}+{{\text{p}}_{\text{i}}}{{\text{U}}_{\text{i-2,n}}}+{{\text{q}}_{\text{i}}}{{\text{U}}_{\text{i,n}}}+{{\text{r}}_{\text{i}}}{{\text{U}}_{\text{i}+\text{2,n}}}={{\text{f}}_{\text{i}}}$    (6)

where,

${{\text{R}}_{\text{i}}}={{\text{a}}_{\text{i}}}^{*}\sum\limits_{\text{j}=\text{6}}^{\text{i}-2}{\text{g}_{\text{j}}^{\beta }\left( {{\text{U}}_{\text{i-j}+\text{2,n}}}-2{{\text{U}}_{\text{i-j,n}}}+{{\text{U}}_{\text{i-j-2,n}}} \right)}$,${{\alpha }_{\text{i}}}=\left( -\text{a}_{\text{i}}^{\text{*}}\text{g}_{\text{2}}^{\beta } \right)$,${{\text{s}}_{\text{i}}}=\left( -{{\text{a}}_{\text{i}}}^{*}\text{g}_{\text{1}}^{\beta }+2\text{a}_{\text{i}}^{\text{*}}\text{g}_{\text{2}}^{\beta } \right)$,${{\text{p}}_{\text{i}}}=\left( \text{b}_{\text{i}}^{\text{*}}-\text{a}_{\text{i}}^{\text{*}}\text{g}_{\text{2}}^{\beta }+2\text{a}_{\text{i}}^{\text{*}}\text{g}_{\text{1}}^{\beta }-\text{a}_{\text{i}}^{\text{*}} \right)$, ${{\text{q}}_{\text{i}}}=\left( -\text{a}_{\text{i}}^{\text{*}}\text{g}_{\text{1}}^{\beta }+2\text{a}_{\text{i}}^{\text{*}}+\left( \lambda -c_{i}^{*} \right) \right)$, ${{\text{r}}_{\text{i}}}=\left( -\text{a}_{\text{i}}^{\text{*}}-\text{b}_{\text{i}}^{\text{*}} \right).$

Then Eq. (6) can be used to construct a linear system in matrix form as:

$\text{A}\underset{\text{ }\!\!\tilde{\ }\!\!\text{ }}{\mathop{\text{U}}}\,=\underset{\text{ }\!\!\tilde{\ }\!\!\text{ }}{\mathop{\text{f}}}\,$       (7)

where,

$A={{\left[ \begin{matrix} {{q}_{2}} & {{r}_{2}} & {} & {} & {} & {} & {} & {} \\ {{p}_{4}} & {{q}_{4}} & {{r}_{4}} & {} & {} & {} & {} & {} \\ {{s}_{6}} & {{p}_{6}} & {{q}_{6}} & {{r}_{6}} & {} & {} & {} & {} \\ {{\alpha }_{8}} & {{s}_{8}} & {{p}_{8}} & {{q}_{8}} & {{r}_{8}} & {} & {} & {} \\ {} & {{\alpha }_{10}} & {{s}_{10}} & {{p}_{10}} & {{q}_{10}} & {{r}_{10}} & {} & {} \\ {} & {} & \ddots & \ddots & \ddots & \ddots & \ddots & {} \\ {} & {} & {} & {{\alpha }_{m-4}} & {{s}_{m-4}} & {{p}_{m-4}} & {{q}_{m-4}} & {{r}_{m-4}} \\ {} & {} & {} & {} & {{\alpha }_{m-2}} & {{s}_{m-2}} & {{p}_{m-2}} & {{q}_{m-2}} \\\end{matrix} \right]}_{\left( m-2 \right)x\left( m-2 \right)}}$

$\underset{\tilde{\ }}{\mathop{\text{U}}}\,={{\left[ \begin{matrix} {{\text{U}}_{\text{2,}1}} & {{\text{U}}_{4,1}} & {{\text{U}}_{6,1}} & \cdots & {{\text{U}}_{\text{m}-4,1}} & {{\text{U}}_{\text{m}-2,1}} \\ \end{matrix} \right]}^{\text{T}}}$,

$\underset{\tilde{\ }}{\mathop{\text{f}}}\,={{\left[ \begin{matrix} {{\text{f}}_{2}}-{{\text{p}}_{2}}{{\text{U}}_{2,1}} & {{\text{f}}_{4}}+{{\text{s}}_{4}}{{\text{U}}_{41}} & {{\text{f}}_{6}}+{{\alpha }_{6}}{{\text{U}}_{6,1}} & {{\text{f}}_{8}}+{{\text{R}}_{i}} & \cdots & {{\text{f}}_{\text{m}-4,1}}+{{\text{R}}_{m-4}} & {{\text{f}}_{\text{m}-2,1}}-{{\text{p}}_{\text{m}-2}}{{\text{U}}_{\text{m},1}} \\ \end{matrix}+{{\text{R}}_{\text{m-2}}} \right]}^{\text{T}}}$

3. HSPGS Methods

Before applying the HSPGS iterative method, we need to transform the original linear system (7) into the preconditioned linear system.

${{A}^{*}}\underset{\tilde{\ }}{\mathop{x}}\,=\underset{\tilde{\ }}{\mathop{{{f}^{*}}}}\,$     (8)

where,

${{A}^{*}}=PA{{P}^{T}}$,and $\underset{\tilde{\ }}{\mathop{{{f}^{*}}}}\,=P\underset{\tilde{\ }}{\mathop{f}}\,$,$\underset{\tilde{\ }}{\mathop{U}}\,={{P}^{T}}\underset{\tilde{\ }}{\mathop{x}}\,$ .

Actually, the matrix P is called a preconditioned matrix and defined as [14-16] $P=I+S$.

where,

$S={{\left[ \begin{matrix} 0 & -{{r}_{1}} & 0 & 0 & 0 & 0 \\ 0 & 0 & -{{r}_{2}} & 0 & 0 & 0 \\ 0 & 0 & 0 & -{{r}_{3}} & 0 & 0 \\ 0 & 0 & \ddots & \ddots & \ddots & 0 \\ 0 & 0 & 0 & 0 & 0 & -{{r}_{m-1}} \\ 0 & 0 & 0 & 0 & 0 & 0 \\\end{matrix} \right]}_{\left( m-1 \right)x\left( m-1 \right)}}$

and the matrix I is an identical matrix. To formulate HSPGS method, let the coefficient matrix A* in (7) be expressed as summation of the three matrices

${{A}^{*}}=D-L-V$      (9)

where, D, L and V are diagonal, lower triangular and upper triangular matrices respectively. By using Eq. (9) and (11), the formulation of HSPGS iterative method can be defined generally as [11, 17, 18]:

${{\underset{\tilde{\ }}{\mathop{x}}\,}^{\left( k+1 \right)}}={{\left( D-L \right)}^{-1}}V{{\underset{\tilde{\ }}{\mathop{x}}\,}^{\left( k \right)}}+{{\left( D-L \right)}^{-1}}{{\underset{\tilde{\ }}{\mathop{f}}\,}^{*}}$     (10)

where, ${{\underset{\tilde{\ }}{\mathop{x}}\,}^{\left( k+1 \right)}}$ represents an unknown vector at (k+1)th iteration. The implementation of the HSPGS iterative method can be described in Algorithm 1.

 Algorithm 1: HSPGS method i. Initialize $\tilde{U}\leftarrow 0$and $\varepsilon \leftarrow {{10}^{-10}}$. ii. For $j=1,2, \ldots, n$  Implement For $j=1,2, \ldots, m-1$ calculate ${{\underset{\tilde{\ }}{\mathop{x}}\,}^{\left( k+1 \right)}}={{\left( D-L \right)}^{-1}}V{{\underset{\tilde{\ }}{\mathop{x}}\,}^{\left( k \right)}}+{{\left( D-L \right)}^{-1}}{{\underset{\tilde{\ }}{\mathop{f}}\,}^{*}}$  ${{\underset{\tilde{\ }}{\mathop{U}}\,}^{\left( k+1 \right)}}={{P}^{T}}{{\underset{\tilde{\ }}{\mathop{x}}\,}^{\left( k+1 \right)}}$  Convergence test. If the convergence criterion i.e $\| {{\underset{\tilde{\ }}{\mathop{U}}\,}^{\left( k+1 \right)}}={{\underset{\tilde{\ }}{\mathop{U}}\,}^{\left( k \right)}} \| \leq \varepsilon=10^{-10}$ is satisfied, go to Step (iii). Otherwise go back to Step (ii). iii Display approximate solutions.
4. Inding Numerical

We have examples of the SFPDE’s to verify the effectiveness of the HSPGS methods. In comparison, three criteria such as number iterations, the execution time (seconds) and maximum error at three different values of $\beta =1.2,\ \beta =1.5\ \text{and }\beta =\text{1}\text{.8}$. During the implementation of the point iterations, the convergence test considered the tolerance error, $\varepsilon ={{10}^{-10}}$.

Example 1 [19]:

Let us consider the following space-fractional initial boundary value problem

$\frac{\partial \text{U}\left( \text{x,t} \right)}{\partial \text{t}}=\text{d}\left( \text{x} \right)\frac{{{\partial }^{\beta }}\text{U}\left( \text{x,t} \right)}{\partial {{\text{x}}^{\beta }}}+\text{p}\left( \text{x,t} \right),$     (11)

Example 2 [19]:

Let us consider the following space-fractional initial boundary value problem

$\frac{\partial \text{U}\left( \text{x,t} \right)}{\partial \text{t}}=\Gamma (1.2){{\text{x}}^{\beta }}\frac{{{\partial }^{\beta }}\text{U}\left( \text{x,t} \right)}{\partial {{\text{x}}^{\beta }}}+3{{\text{x}}^{\text{2}}}\left( \text{2x-1} \right){{\text{e}}^{\text{-t}}},$     (12)

All numerical results for Eqns. (11) and (12), obtained from application of GS, FSPGS and HSPGS iterative methods are recorded in Table 1 and 2 by using the different value of mesh size, M=128, 256, 512, 1024 and 2048.

Table 1. Comparison between number of iterations (K), the execution time (seconds) and maximum errors for the iterative methods using example at β=1.2, 1.5, 1.8

 M Method β=1.2 β=1.5 β=1.8 K Time Max Error K Time Max Error K Time Max Error 128 FSPGS 36 1.09 2.37e-02 104 2.83 6.20e-04 345 9.48 3.99e-02 HSPGS 19 0.26 2.37e-02 42 1.24 6.20e-04 108 3.25 4.60e-02 256 FSPGS 72 7.23 2.44e-02 272 27.00 5.69e-04 1123 111.98 3.97e-02 HSPGS 36 2.50 2.44e-02 104 11.33 5.69e-04 345 44.05 4.59e-02 512 FSPGS 151 58.11 2.47e-02 723 276.20 5.36e-04 3659 1398.43 3.96e-02 HSPGS 72 23.35 2.47e-02 272 124.86 5.36e-04 1123 478.23 4.55e-02 1024 FSPGS 328 492.56 2.49e-02 1935 945.20 5.13e-04 11836 2138.11 3.95e-02 HSPGS 151 193.63 2.49e-02 724 473.13 5.13e-04 3657 1054.31 4.53e-02 2048 FSPGS 1547 1227.21 2.50e-02 8320 4348.68 5.02e-04 47322 8979.18 3.93e-02 HSPGS 327 472.53 2.50e-02 1938 3120.96 5.02e-04 22152 4335.75 4.51e-02

Table 2. Comparison between number of iterations (K), the execution time (seconds) and maximum errors for the iterative methods using example at β=1.2, 1.5, 1.8

 M Method β=1.2 β=1.5 β=1.8 K Time Max Error K Time Max Error K Time Max Error 128 FSPGS 27 0.72 1.80e-01 75 1.83 5.44e-02 213 5.27 8.88e-04 HSPGS 15 0.25 1.80e-01 30 0.54 5.44e-02 67 2.94 8.88e-04 256 FSPGS 55 4.72 1.84e-01 197 17.11 5.58e-02 686 59.48 4.09e-04 HSPGS 27 1.38 1.84e-01 75 7.83 5.58e-02 213 20.45 4.09e-04 512 FSPGS 116 37.86 1.86e-01 522 170.92 5.65e-02 2213 737.50 1.54e-04 HSPGS 55 10.51 1.86e-01 197 77.58 5.65e-02 686 331.95 1.54e-04 1024 FSPGS 250 322.55 1.89e-01 1435 443.81 5.69e-02 3452 820.62 1.49e-04 HSPGS 116 147.81 1.89e-01 522 299.59 5.69e-02 1224 411.91 1.49e-04 2048 FSPGS 518 413.21 1.88e-01 4125 713.64 5.85e-02 5127 3173.73 1.20e-04 HSPGS 251 207.81 1.88e-01 1437 311.27 5.85e-02 2253 1062.72 1.20e-04
5. Discussion and Conclusion

In order to get the numerical solution of the space-fractional diffusion problems, the paper presents the derivation of the Caputo’s implicit finite difference approximation equations in which this approximation equation leads a linear system. From observation of all experimental results by imposing the GS, FSPGS and HSPGS iterative methods, it is obvious at β=1.2 that number of iterations have declined approximately by 41.30-82.45% corresponds to the HSPGS iterative method compared with the GS and FSPGS method. Again, in terms of execution time, implementations of HSPGS method are much faster about 51.18-92.43% than the GS and FSPGS method. It means that the HSPGS method requires the least amount for number of iterations and computational time at β=1.2 as compared with GS and FSPGS iterative methods. Based on the accuracy of both iterative methods, it can be concluded that their performance numerical solutions are in good agreement.

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