On convergence and error analysis of the parametric iteration method |
|
S. A. Saeed Alavi 1*, Aghileh Heydari 1, Farhad Khellat 2 |
|
1 Department of Mathematics, Payame Noor University, Tehran, I.R of Iran 2 Department of Mathematic, Faculty of Mathematical sciences, Shahid Beheshti University, Evin-Tehran, Iran *Corresponding author E-mail: alavi601@yahoo.com |
Copyright © 2015 S. A. Saeed Alavi et al. 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.
Parametric iteration method falls under the category of the analytic approximate methods for solving various kinds of nonlinear differential equations. Its convergence only for some special problems has been proved. However in this paper, an analysis of error is presented, then due to it, the convergence of method for general problems is proved. To assess the performance of the claimed error bound and also the convergence of the method, numerical experiments are presented performed in MATLAB 2012b.
Keywords: He’s Variational Iteration Method; Parametric Iteration Method; Convergence; Error Bound.
1. Introduction
Parametric iteration method (PIM) is an analytic approximate method for solving linear and nonlinear problems proposed in [1]. At beginning it was proposed for solving nonlinear fractional differential equations by modifying He’s variational iteration method (VIM) [2]. The PIM enjoys some augmented factors which made it more completed than the VIM. In fact by adjusting these factors one can establish more accurate approximations in comparison with the VIM.
During recent decade, many researchers have been worked on the VIM for solving various kinds of problems which mentioning them is out of the scope of this paper. Besides, some authors have been centered on the convergence of the VIM for some specific problems, like for multi-order fractional DE’s [3], multi-delay DE’s [4], ODE’s [5], systems of ODE’s [6] and etc. Herein, the work of Odibat [7] is more interesting and different because of its generality. In fact, he concluded the convergence of the VIM by introducing a semi-contraction operator and completed the proof like the proof of the Banach’s fixed point theorem.
On the other hand, the PIM was utilized for solving various kind of differential equations like Abel equation [8], nonlinear chaotic Genesio system [9], boundary value problems [10], linear optimal control problems [11] and etc. Convergence theorem for some particular cases was discussed in some of these literature (e.g. [8,9]), but what is still missing is a proof of the convergence of the PIM for a general differential equation. Also, from the both theoretical and practical viewpoint, another necessary talk is a complete discussion about the error bound of the approximations. Therefore, the goal of this article is to establish an error term and then presenting a general proof of the convergence of the PIM.
2. Parametric iteration method (PIM)
To explain the basic idea of the PIM considers the following differential equation:
(1)
Where is a nonlinear operator,
denotes the time, and
is an unknown variable. First
consider (1) as below:
(2)
Where and
denote linear and nonlinear
differential operator of the unknown
respectively, and
is the source term. We then
construct a family of iterative formulas as:
(3)
where . In this formula
and
denote the so-called auxiliary
parameter and auxiliary function respectively. In this work we take
Accordingly, the successive approximations
will be readily obtained by choosing
the zeroth component
. (For more details about PIM see
[1]).
3. Error analysis and convergence
Consider the following nonlinear problem
(4)
Where and
are defined as:
(5)
Where and
are continuous real functions on
. In
we use the infinity norm i.e. for
vector
we have
and for every
we use the maximum norm as
. Also the norm of vector functions
like
is:
(6)
In order to use the PIM, we rewrite (4) as
(7)
Where is an auxiliary linear operator and
is the nonlinear operator. Then the
constructed iteration formula by PIM will be defined by:
(8)
Taking and choosing the initial
approximation
in the above sequence, clearly we
can say that
. So the following lemma will be
obtained.
Lemma 3.1: For every and for every
(9)
Now the iteration formula (8) can be written as
(10)
Let’s denote nth approximation by and
then the convergence of
is due to the norm
(11)
Before presenting the main theorem, we restate the Lipschitz
condition for the vector function . Suppose that for every the
component
of function
, there exist a positive real
constant
such that for every
and for every
and
the following condition satisfies:
(12)
In this situation, letting we can say that
satisfies a Lipschitz condition with
respect to the first argument with the Lipschitz constant L, i.e.
(13)
Theorem 3.2: Assume that is continuous on
where
and satisfies a Lipschitz condition
on
with respect to the first argument
with the Lipschitz constant L. Also suppose that
is bounded on
to a positive real number
. Then for two arbitrary successive
approximations we have
(14)
Proof: If we denote the approximate solution obtained by the first
iteration with and
, according to (10) and noticing that
where
and
we can write
(15)
Now, let in (10), using the notation
, we have
We rearrange the final statement as bellow
(16)
And similarly
(17)
In summary, this argument will lead to the following general form
(18)
The maximum of left hand side on index satisfies (18) too, due to the fact
that the right hand side
of (18) is independent from index
. So, due to the defined norm (6),
taking maximum of both side of (18) on all
, we have:
(19)
This completes the proof. ■
Now we want to prove that if we choose such that
then the right hand side of (14)
vanishes when n tends to infinity. First we prove an auxiliary lemma.
Lemma 3.3: Assume that then for every
where
we have
(20)
Proof: For a fixed we have
. So
(21)
Also for every real number and
we know that
. Therefore noticing to the
assumption
and taking
will complete the proof. ■
Theorem 3.4: Due to the last assumption we have
(22)
Proof: Let and
. Therefore the sum in (22) can be
written as
(23)
Or
(24)
Noticing to the expansion of the exponential function and for all
, the series
is absolutely convergent and we name
its sum
. Clearly, the limit of
is zero. Also, by lemma 3.3 we know
that
. So, for every given
there exist a
such that for every
we have
. In this case we can write
(25)
In the last inequality absolutely convergence of is used. On the other hand, as
tends to infinity
. So if we keep
fixed and let
then we have
(26)
Since the was arbitrary, the proof is
complete. ■
Theorem 3.2 and 3.4 indicate that for sufficiently large, two successive terms of the
sequence
for every arbitrary
satisfy the following relation
(27)
Using this, one can easily show that is a Cauchy sequence in
. Therefor it is convergent in the
complete space
. So we have:
Corollary 3.5: Due to the last assumptions, the sequence (10) constructed by the PIM is convergent.
Corollary 3.6: The valid region of the convergence-control parameter is
.
4. Numerical experiments
To demonstrate the efficiency of the error bound defined by (14) we consider the following two dimensional test problems.
(28)
Where the time domain is and the exact solutions from [12]
are:
(29)
(30)
In view of (4) and (5) we have:
(31)
is linear and obviously, it
satisfies a Lipschitz condition by Lipschitz constant
. Choosing
, the
is bounded by
and using the notation
for the error bound described in
(14) we have:
(32)
Furthermore the norm of direct difference of two successive
approximations and
appeared in the left hand side of
(14) is denoted by
(33)
Then for everyand for all
the numerical results must confirm
the relation
to ensure that the theoretical
result of the Theorem 3.2 is reliable.
Also in order to discuss the convergence of the PIM claimed
in the Corollary 3.5, we denote the absolute error by and define it by
(34)
In Fig.1, we plot ,
and
obtained by the PIM with
and
. Other different values of
and
is discussed in Fig.2 and Table 1.
Analysis of the Fig.1:
Error bound: As could be seen from the plot of , the estimated error bound is
really an upper bound for
which confirms the inequality (14).
This is true for every iteration as zoomed out part shows. Plot of
also shows that by increasing
the error bound vanishes which is a
confirmation of theorem 3.4.
Cauchy sequence: The plot of shows that
when
grows. This means that for every
and for sufficiently large
,
which confirms (27). The latter can
be used to conclude the sequence
is a Cauchy sequence.
Convergence: The plot of shows that for sufficiently large
,
, and this is a confirmation of convergence of the sequence
constructed by the PIM.
In Fig.2, we plot and
for the solutions of the PIM by
various
and
. As could be seen the error bound
confirms what is claimed in theorem 3.2 and 3.4.
Although Fig.1 and Fig.2 provide us good information of error
bound, but what is doesn’t show is a specified data for the convergence rate.
So to study of the convergence rate we report the final values of and
for
and
in Table 1.
Table 1: Test of Error Bound for Various And
|
|
|
||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
0.001 |
|
Table 1 shows that for the convergence is excellent but for
we need much more iterations to
conclude the convergence. For
, although
seems to be appropriate for
vanishing
but bigness of
in the same formula (32) makes
very big. On the other hand, for
, similarly
but
is not very big in opposite of the
case
, consequently the convergence is
faster. From this viewpoint,
seems to be the best choice since
, however there exist many
counterexamples in nonlinear problems showing that some values other than
can give better approximations. Such
an argument leads to a known problem that is finding an optimal value of
accelerating parameter
which in general is an open problem
in this field. By the results of this paper a proposal is minimizing the error
term as a function of the parameter
as a variable which is left to the
further works.
5. Conclusion
In this paper, a convergence analysis of the PIM is
presented. This is performed by establishing a novel error bound and showing
this error bound tends to zero. Also an interesting result has been concluded
for the auxiliary parameter h. Although finding optimal h in general is an open
problem, but we hope that the results of this paper are a promising tools for
researchers. Our proposal is to find optimal h by minimizing the presented
error term as a function of, which is left to the further works.
References
[1] A. Ghorbani, “Toward a New Analytical Method for Solving Nonlinear Fractional Differential Equations”, Comput. Meth. Appl. Mech. Engrg. Vol.197, (2008), pp: 4173-4179. http://dx.doi.org/10.1016/j.cma.2008.04.015.
[2] J. H. He, “Variational iteration method – a kind of non–linear analytical technique: some examples”, Int. J. Non–Linear Mech. Vol.34, (1999), pp: 699-708. http://dx.doi.org/10.1016/S0020-7462(98)00048-1.
[3] S. Yang, A. Xiao, H. Sua, “Convergence of the variational iteration method for solving multi-order fractional differential equations”, Computers and Mathematics with Applications, Vol.60, (2010), pp: 2871–2879. http://dx.doi.org/10.1016/j.camwa.2010.09.044.
[4] S. Yang, A. Xiao, “convergence of variational iteration method for solving multi-delay differential equations”, computers & mathematics with applications, Vol.61, No.8, (2011), pp: 2148-2151.
[5] E. Yusufoğlu, “Two convergence theorems of variational iteration method for ordinary differential equations”, Appl. Math. Lett. (2011), http://dx.doi.org/10.1016/j.aml.2011.02.005.
[6] D. Khojasteh,”convergence of variational iteration method for solving linear systems of ODE’s with constant coefficients”, computers & mathematics with applications, Vol.56, No.8,(2008),pp: 2027-2033.
[7] Z. M. Odibat, “A study on the convergence of variational iteration method,” Mathematical and Computer Modelling Vol.51, (2010), pp: 1181_1192.
[8] J. Saberi-Nadjafi, A. Ghorbani, “Piecewise-truncated parametric iteration method: a promising analytical method for solving Abel differential equations", Z. Naturforsch. Vol.65a, (2010), pp: 529-539.
[9] A. Ghorbani and J. Saberi-Nadjafi, “A Piecewise-Spectral Parametric Iteration Method for Solving the Nonlinear Chaotic Genesio System”, Mathematical and Computer Modeling, Vol.54, (2011), pp: 131-139. http://dx.doi.org/10.1016/j.mcm.2011.01.044.
[10] A. Ghorbani, M. Gachpazan, J. Saberi-Nadjafi, “A modified parametric iteration method for solving nonlinear second order BVPs”, Comput. Appl. Math. Vol.30, No.3, (2011), pp: 499-515. http://dx.doi.org/10.1590/S1807-03022011000300002.
[11] A. S. Alavi, A. Heydari, “Parametric Iteration Method for Solving Linear Optimal Control Problems”, Applied Mathematics, Vol.3, (2012), pp: 1059-1064. http://dx.doi.org/10.4236/am.2012.39155.
[12] C. K. Chui and G. Chen, “Linear Systems and Optimal Control”, Springer-Verlag, Berlin, Heidelberg, (1989), pp:76-80 http://dx.doi.org/10.1007/978-3-642-61312-8.