Journal of Applied Mathematics and Physics
Vol.04 No.01(2016), Article ID:63066,9 pages
10.4236/jamp.2016.41017
The Tikhonov Regularization Method in Hilbert Scales for Determining the Unknown Source for the Modified Helmholtz Equation
Lei You, Zhi Li, Juang Huang, Aihua Du
College of Science, Guangdong Ocean University, Zhanjiang, China

Copyright © 2016 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/



Received 9 December 2015; accepted 23 January 2016; published 27 January 2016
ABSTRACT
In this paper, we consider an unknown source problem for the modified Helmholtz equation. The Tikhonov regularization method in Hilbert scales is extended to deal with ill-posedness of the problem. An a priori strategy and an a posteriori choice rule have been present to obtain the regularization parameter and corresponding error estimates have been obtained. The smoothness parameter and the a priori bound of exact solution are not needed for the a posteriori choice rule. Numerical results are presented to show the stability and effectiveness of the method.
Keywords:
Ill-Posed Problem, Unknown Source, Regularization Method, Discrepancy Principle in Hilbert Scales

1. Introduction
A variety of important problems in science and engineering involve the solution to the modified Helmholtz equation, e.g., in implicit marching schemes for the heat equation, in Debye-Huckel theory, and in the linearization of the Poisson-Boltzmann equation [1] - [5] . In this paper, we consider the following problem of determining an unknown source which depends only on one variable for the modified Helmholtz equation [6] :
(1)
where
is the unknown source and
is the supplementary condition and the constant
is the wave number. Our purpose is to identify the source term
from the input data
. This
problem is called the inverse source problem. In practice, the data at
are often obtained on the basis of reading of physical instrument. So only a perturbed data
can be obtained. We assume that the exact and measured data satisfy
(2)
where
denotes the noise level,
denotes the
―norm.
Inverse source problems arise in many branches of science and engineering, e.g., heat conduction, crack identification electromagnetic theory, geophysical prospecting and pollutant detection. The main difficulty of these problems is that they are ill-posed (the solution, if it exists, does not depend continuously on the data). Thus, the numerical simulation is very difficult and some special regularization is required. Many papers have presented the mathematical analysis and efficient algorithms of these problems. The uniqueness and conditional stability results for these problems can be found in [7] - [12] . Some numerical reconstruction schemes can be found in [13] - [23] .
Up to now, only a few papers for identifying the unknown source on the modified Helmholtz equation have been reported. In [1] , an integral equation method has been proposed and a simplified Tikhonov regularization has been presented in [6] . In this paper, we will use the Tikhonov regularization method to solve the problem (1). Unlike the one in [6] , a different Tikhonov functional will be used and we show that the regularization parameter can be chosen by a discrepancy principle in Hilbert scales which is proposed by Neubauer [24] and better convergence rates have been obtained. Moreover, the smoothness parameter of the exact solution is not needed for the new method.
This paper is organized as follows. In Section 2, we will give the method to construct approximate solution. The choices of the regularization parameter and corresponding convergence results will be found in Section 3. Some numerical results are given in Section 4 to show the effectiveness of the new method.
2. The Tikhonov Regularization Method
Let
, it is well known that
is an orthonormal basis in
, i.e.,

where 



It is easy to derive a solution of problem (1) by the method of separation of variables [6]

where

Note that the exact data 







where 


where

We let 

where 


If we let


So we have

Which means that

Then the approximate solution can be given as

Lemma 1. For any

Lemma 2. [26] For

Lemma 3.

where 

Proof.

The proposition follows by applying (16) with b replaced by
Lemma 4.

where

Proof. With the representation

and Lemma 1, we have

3. The Choices of Regularization Parameter a and Convergence Results
In this section, we consider the choices of the regularization parameter. An a priori strategy and an a posteriori choice rule will be given. Under each choice of the regularization parameter, the convergence estimate can be obtained.
3.1. The a Priori Choice Rule
Take

we can obtain the following theorem.
Theorem 5. If (2) holds and (7) holds with


Proof. With Lemma 3, Lemma 4 and (23) we obtain

Moreover, by using Hölder inequality, we have

Formulae (8) implies that

The assertion of the Lemma follows from (25)-(27).
3.2. The a Posteriori Choice Rule
For any

It is apparent that the function 


So we can get the following lemma
Lemma 6. Let g, 


for some


In the following, we denote the unique 


Lemma 7. Let g, 


a)

b)

where

Proof.
a) Let

then

b)

then from Lemma 1

The rest follows from a).
Theorem 8. Suppose that the conditions (2) and (30) hold, the condition (7) hold with


Proof. By using the triangle inequality we know

So, in terms of Equations (17), (19) and (33), we have

From (26),

Combining (41) and (42), we obtain

The assertion of the theorem follows from (27).
4. Numerical Tests
In this section, we present some numerical tests to check the effectiveness of the method. The discretization knots are




where 

in practical computing. The relative errors are measured by the weighted 

All tests are computed by using Matlab and we will also compare the method (M1) with the method in [6] (M2, notate the approximate function as

where 

Example [6] It is easy to see that the function 








In Table 2, we give a numerical comparison between M1 and M2 with fixed

5. Conclusion
We have proposed a new method to identify the unknown source in the modified Helmholtz equation. Theoretical analysis as well as experience from computations indicates that the proposed method works well.
Table 1. Relative errors for various p and N with
Table 2. Comparison of M1 and M2.
Acknowledgements
The project is supported by the National Natural Science Foundation of China (No. 11201085).
Cite this paper
LeiYou,ZhiLi,JuangHuang,AihuaDu, (2016) The Tikhonov Regularization Method in Hilbert Scales for Determining the Unknown Source for the Modified Helmholtz Equation. Journal of Applied Mathematics and Physics,04,140-148. doi: 10.4236/jamp.2016.41017
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