Research Article  Open Access
S. A. Edalatpanah, M. M. Rashidi, "On the Application of Homotopy Perturbation Method for Solving Systems of Linear Equations", International Scholarly Research Notices, vol. 2014, Article ID 143512, 5 pages, 2014. https://doi.org/10.1155/2014/143512
On the Application of Homotopy Perturbation Method for Solving Systems of Linear Equations
Abstract
The application of homotopy perturbation method (HPM) for solving systems of linear equations is further discussed and focused on a method for choosing an auxiliary matrix to improve the rate of convergence. Moreover, solving of convectiondiffusion equations has been developed by HPM and the convergence properties of the proposed method have been analyzed in detail; the obtained results are compared with some other methods in the frame of HPM. Numerical experiment shows a good improvement on the convergence rate and the efficiency of this method.
1. Introduction
Consider the following convectiondiffusion equation: On the unit square domain , with constant coefficients , , subject to Dirichlet boundary conditions. Discretization by a fivepoint finite difference operator leads to the following linear system: where denotes a vector in a finitedimensional space and . With discretization on a uniform grid, using standard secondorder differences for the Laplacian and either centered or upwind differences for the first derivatives, the coefficient matrix has the form Or with Kronecker product () we obtain where ; are Reynolds numbers. Also, the equidistant stepsize is used in the discretization and the natural lexicographic ordering is employed to the unknowns and the right hand side satisfies . For details, we refer to [1, 2] and the references therein. Convectiondiffusion equations appear in many fields of science and engineering and there are some reliable methods for solving this class of problems ([1, 2] and the references therein). Here, we use alternative approach to solve (2) based on homotopy perturbation method (HPM). The HPM was first proposed by the Chinese mathematician He in 1999 [3] and was further developed and improved by him [4–6]. He presented a homotopy perturbation technique based on the introduction of homotopy in topology coupled with the traditional perturbation method for the solution of algebraic equations and ODEs. This technique provides a summation of an infinite series with easily computable terms, which converges to the solution of the problem. In the literature, various authors have successfully applied this method for many kinds of different problems such as nonlinear partial differential equations [7, 8], nonlinear wave equations [5], nonlinear integral and integrodifferential equations [9], fractional IVPs [10], and optimization [11]. Nevertheless, to our knowledge, a few papers have considered this analytical method for system of linear equations. Keramati [12] presented an efficient method for solving system of linear equations based on HPM. Liu [13] by using HPM and splitting methods proposed a new method for linear systems. Saberi Najafi et al. [14] based on HPM proposed a new algorithm for fuzzy linear systems and compared it with Adomian’s decomposition method. They also in [15] show that solving linear systems by using a new method called modified HPM, presented by Noor et al. [16], is impractical.
In this paper, by combination of HPM and preconditioning technique, we will present a new model for solving linear systems and apply this method to the above convectiondiffusion equation.
2. Homotopy Perturbation Method for Linear Systems
Consider linear system (2). HPM procedure for solving of this system is a continuous map from the interval to a function space where it is as follows: and is an embedding parameter.
Keramati [12] applied a HPM to solve linear systems by the following definitions.
Let and , where is a known vector. Then homotopy is Obviously, we will have According to the HPM, we can first use the embedding parameter as a small parameter and assume that the solution of (2) can be written as a power series in : and the exact solution is obtained as follows: Putting (8) into (5) and comparing the coefficients of identical degrees of on both sides, we find And in general Taking yields Therefore, the solution can be of the form This method is efficient for solving system of linear equations but, however, does not converge for some systems when the spectral radius is greater than one. To make the method useful, Liu in [13] added the auxiliary parameter and the auxiliary matrix to the HPM. This method is as follows: where Here, is an auxiliary parameter, is the auxiliary matrix, and the operator is decided by the splitting matrix . Then his iterative scheme for is Therefore, And finally by setting , he obtained the solution as follows: This author has adapted the Richardson, Jacobi, and the GaussSeidel methods to choose the splitting matrix and obtained that the homotopy series converged rapidly for a large sparse system with a small spectral radius. But to improve the rate of convergence, he does not present on a method for choosing (see [13], conclusion). The main goal on this paper is studying this problem and doing some modifications for having very effective and straightforward results.
Our idea for choosing is preconditioning technique. Preconditioning methods are the most authoritative techniques to improve poor properties of the basic iterative methods. The main aim of preconditioning method for is to substitute the original matrix with an equivalent one which has better properties concerning the computation of a solution (generally by a certain iterative method). The two matrices are equivalent in the sense that they have the same solution. Simple preconditioners of this type are the left matrix preconditioners. The left preconditioned matrix is a nonsingular matrix and the preconditioned matrix is defined by , where .
In this paper, we consider as a preconditioner and if the auxiliary matrix is properly chosen, we can be able to achieve the best results. To improve the convergence rate of the basic iterative method, various models of preconditioning systems have been proposed [17–19]. In the literature, various authors have suggested different models of Type preconditioned [20–28], where all of these models are left preconditioners. We can consider all of the models in [17–28] for .
Here we consider Usui et al. [22] model of )Type preconditioners for , where Let , where is the diagonal matrix and , are strictly lower and strictly upper triangular matrices of , respectively. Then the preconditioned matrix is as follows: We know that , where Then we have Thus, where , , and are, respectively, the diagonal, strictly lower, and strictly upper triangular parts of .
Therefore, we have Furthermore, for more convergence speed in JacobiHPM and GaussSeidelHPM we choose as follows.
In JacobiHPM, In GaussSeidelHPM, Now, we will show that by this choice of , these methods are faster than of the basic methods form point of view of the convergence speed.
Definition 1 (see [29, 30]). (a) A matrix is called a matrix if for any , .
(b) A matrix is an matrix, if is nonsingular and .
Definition 2 (see [29, 30]). Let be a real matrix. The splitting is called(a)convergent if ( is the spectral radius of matrix);(b)regular if and .
Lemma 3 (see [29]). Let A be a matrix. Then is matrix if and only if there is a positive vector such that .
Lemma 4 (see [31]). Let , be matrix and an matrix; if , then is an matrix too.
Lemma 5 (see [32]). Let be a regular splitting. Then if and only if is nonsingular and is nonnegative.
Lemma 6 (see [24], Theorem 2.7, Remark 2.20). Let and be regular splittings of and where is (19). If is matrix, then By applying Lemma 3, we can achieve the following results.
Theorem 7. Let be matrix. If we use in (19) and in (25) and (26), then convergence rate of (18) is faster than of the convergence rate in , or .
Proof. Since is an matrix, by Lemma 3 it is easy to see that also is matrix, and thus by Lemma 4 is matrix and is nonnegative. Then by Lemmas 5 and 6 we can obtain .
Therefore by [13, Theorem 2.1] the proof is completed.
3. Numerical Experiments
In this section, we give an example to illustrate the obtained results in the previous sections.
We test convectiondiffusion equation (1) when , .
Then, we solved the matrix yielded by JacobiHPM and GaussSeidelHPM.
In this experiment, we choose the right hand side vector, such that is solution of . The stopping criterion with tolerance is .
In Tables 1 and 2 we report the CPU time and number of iterations (Iter) for the JacobiHPM and GaussSeidelHPM methods, respectively.


From the tables, we can see that if is (19), then the mentioned methods are superior to the basic methods.
4. Conclusions
In this paper, we have applied a new method for solving convectiondiffusion equations based on the HPM. We can see that how HPM method is influenced for solving system of linear equation, if combined by preconditioning technique. Finally, from theoretical speaking and numerical experiment, it may be concluded that the convergence behaviors of this method is superior to some other methods in the frame of HPM.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
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Copyright © 2014 S. A. Edalatpanah and M. M. Rashidi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.