1. Introduction
Let
be a nonlinear monotone operator, i.e.,
defined on the real Hilbert space
. Here and below
and
, respectively, denote the inner product and corresponding norm in
and
, respectively, denote open and closed ball in
with center
and radius
We are concerned with finite dimensional approximation of the ill-posed equation
which has a solution
for exact data
However, we have
for some
are the available data, such that
Due to the ill-posedness of (
1), one has to apply regularization method to obtain an approximation for
For (
1) with monotone
Lavrentiev regularization (LR) method is widely used (see [
1,
2,
3,
4,
5,
6]). In (LR) method the solution
of the equation
is used as an approximation for
Here (and below)
is an initial approximation of
with
for some
The solution of (
3), with
y in place of
is denoted by
, i.e., (cf. [
5])
Let
and
be as in Equations (
3) and (
4), respectively. Then, we have the following inequalities (cf. [
5]).
and hence,
and
For proving our result, we assume that, either
is self-adjoint or
is positive type, i.e.,
and
(see [
7]). Here and below
is the Fréchet derivative of
(if
is self-adjoint, then
).
Remark 1. If is positive type, then Further as in [8] (Lemma 2.2) one can prove So, the results in this paper hold for positive type operator up to a constant. Therefore, for convenience, hereafter we assume is self-adjoint.
In earlier studies such as [
4,
5,
6,
9,
10], the following source condition:
or
was used to obtain an estimate for
In fact, if the source condition (
8) is satisfied, then, we have [
5]
and if (
9) is satisfied, then, we have [
2]
In this study, we introduce a new source condition,
where
and
We shall use this source condition (
10) to obtain a convergence rate for
and to introduce a new parameter-choice strategy.
Remark 2. (a) Note that in a posteriori parameter-choice strategy, the regularization parameter α (depending on δ and ) is chosen at the time of computing (see [11]). The new source condition (10) is used to choose the parameter α (depending on δ and ) and independent of before computing (see Section 2) and also it gives the best known convergence order (see Remark 4). This is the innovation of our approach. (b) Notice that, the operator A and are used to obtain an estimate for In actual computation of the approximation (see Equation (38)) and α (see Section 4) we do not require the operator A or The following formula ([
12], p. 287) for fractional power of positive type operators
is used in our analysis.
where
and
z is a complex number such that
Let
and
. Then, we have
Note that, if
is self-adjoint, then,
A is self-adjoint. Further, suppose
is positive type, then we have
i.e.,
A is positive type.
Next, we shall prove that (
10) implies
for some constants
and
. For this, we use the standard non-linear assumptions in the literature (cf. [
4,
13]).
Assumption 1. For every
and
there exists
and an element
with
and
Suppose (
10) holds for
then
so by the definition of
A and Assumption 1, we have
where
Further note that
Suppose
where
Observe that
So
implies
i.e., (
10) implies (
12). Similarly one can show that (
10) implies
for some constant
Throughout the paper, we use the relation (Fundamental Theorem of Integration),
for all
x and
u in a ball contained in
Remark 3. In general, it is believed that (see [5]) a priori parameter-choice strategy is not a good strategy to choose α since the choice is depending on the unknown In this study, we introduce a new parameter-choice strategy which is not depending on unknown ν and gives the best known convergence order In some recent papers, the first author and his collaborators considered iterative methods [
14,
15] for obtaining stable approximate solutions for (
3) (see [
8,
16]). In most of the iterative methods Fréchet derivative of the operator involved is used. In [
10], Semenova considered the iterative method defined for fixed
by
Note that, the above iterative method is derivative-free. Convergence analysis in [
10] is based on the assumption that
is Lipschitz continuous and the Lipschitz constant
R satisfies
where
is a constant. Contraction mapping arguments are used to prove the convergence in [
10].
In [
16], George and Nair considered the method (
13), but with
independent on the regularization parameter
and the Lipschitz constant
instead of
The source condition on
in [
16] depends on the known
and the analysis in [
16] is not based on the contraction mapping arguments as in [
10].
The purpose of this paper is threefold: (1) introduce a new source condition, (2) introduce a new parameter-choice strategy, and (3) apply the parameter-choice strategy to the (finite–dimensional setting of the) method in [
16].
The remainder of the paper is organized as follows. In
Section 2, we present the error bounds under the source condition (
10) and a new parameter-choice strategy. In
Section 3, we present the finite dimensional realization of method (
13). In
Section 4, we present the finite dimensional realization of (
10).
Section 5 contains the numerical example and the conclusion is given in
Section 6.
3. Finite Dimensional Realization of (13)
Consider a family
of orthogonal projections of
onto the range
of
Let there exists
such that
and let
We assume that;
- (i)
- (ii)
there exists
such that
- (iii)
there exists
such that
Remark 5. - (a)
Suppose is self-adjoint for Then, and by Assumption 1, we have Hence,so, Therefore, in this case, we can take,
- (b)
Suppose, is not self-adjoint for In this case, under the additional assumption (see [17])with we have
Therefore, in this case, we can take,
From now on, we assume and with
First we shall prove that
has a unique solution
under the assumption
Proposition 1. Suppose (35) holds. Then (34) has a unique solution in for all and Proof. Since
is monotone, we have
so
is monotone and
Hence by Minty–Browder Theorem(see [
18,
19]), Equation (
34) has a unique solution
for all
and
Next, we shall prove that
Note that by (
34), we have
Let
Then by (
36), we have
or
So, we have
and hence
i.e.,
□
The method: The rest of this section,
is assumed to be positive self-adjoint operator. We consider the sequence
defined iteratively by
where
Note that if
exists, then the limit is the solution
of (
34).
Theorem 9. Let and are solutions of (3) and (34), respectively. Then Proof. Note that by (
3), we have
Therefore, by (
34) and (
39), we have
Let
Then by (
40), we have
or
Notice that
that is
So,
is self-adjoint and hence by (
41),
and
Since
by (
42) and (
43), we have
□
Remark 6. If and then by Theorems 2 and 9, we have Theorem 10. Let and Then, and Furtherwhere and . Proof. We shall show the following using induction;
- (1a)
- (1b)
the operator
is positive self-adjoint, well defined and
- (1c)
Clearly,
Furthermore, we have by Proposition 1,
so by (
32),
is a well defined and positive self-adjoint operator with
So (1a) and (1b) hold for
Since
is a positive self-adjoint operator ( cf. [
20]),
and since
and
, we have
Thus, So, for (1a)–(1c) hold. The induction for (1a)–(1c) is completed, if we simply replace in the preceding arguments with , respectively. The result now follows from (1c). □
Theorem 11. Let with . Let and be solutions of (3) and (4), respectively. For and let be as in (38). Letand Proof. By Theorems 9 and 10, we have
Here, we used the fact that for and Thus, we obtain the required estimate in the theorem. □
Finite dimensional realization of (
20) is considered next.
4. Finite Dimensional Realization of the a New Parameter Choice Strategy (20)
The proof of the next theorem is similar to that of Theorem 3, so the proof is omitted.
Theorem 12. For each the function for defined in (49), is continuous, monotonically increasing and In addition to (
2), we assume that
for some
and
. The proof of the following theorem follows from the intermediate value theorem.
Theorem 13. If satisfies (2) and (50). Then,has a unique solution Next, we shall show that if
satisfies (
51), then
Our proof is based on the moment inequality (
21).
Theorem 14. Let Assumption 1 and (10) be satisfied and let satisfies (51). Then, Proof. By (
24), the result follows once we prove
This can be seen as follows,
where, we used
Next, we shall show that
is bounded. Note that,
so, we have
and hence
Now, the result follows from (
27) and (
53). □
Theorem 15. Suppose Assumption 1 and (10) hold and if is chosen as a solution of (51). Then, Proof. Let
Then, similar to (
24), we have
where
Note that,
so
Therefore, by (
10), (
55) and (
56), we have
or
□
By combining Theorems 11, 14 and 15, we have the following Theorem.
Theorem 16. Suppose Assumption 1 and (10) hold and if is chosen as a solution of (51). Then Remark 7. Note that in the proposed method a system of equation is solved to obtain the parameter α and used it for computing Whereas in the classical discrepancy principle one has to compute α and in each iteration step. This is an advantage of our proposed approach.
5. Numerical Examples
The following steps are involved in the computation of
Step I Compute
satisfying (
51)
Step II Choose n such that
Step III Compute
using (
38).
To compute
consider a sequence
of finite dimensional subspaces, where
with
as the linear splines (in a uniform grid of
points in
), so that dimension
Since
are some scalars. Then, from (
38), we have
where
is the projection on to
with
In this case one can prove as in [
21] that
So we have taken
in our computation. Since
we approximate
where
are grid points. So
satisfies (
58), if
satisfies the equation
where
and
To compute the
satisfying (
51), we follow the following steps:
Let Then so for some scalars Note that or where
Since
we have
Further
and
satisfies the equations
and
respectively, where
and
We compute
in (
51), using Newton’s method as follows. Let
Then
where
Let
The
satisfies the equation
Then, using Newton’s iteration we compute the iterate as; In our computation, we stop the iterate when
We consider a simple one dimensional example studied in [
5,
7,
22,
23] to illustrate our results in the previous sections. We also compare our computational results with that adaptive method considered in [
16,
24]. Let us briefly explain the adaptive method considered in [
16]. Choose
For each
j find
such that
Choose, as the regularization parameter.
Example 1. Let be a constant. Consider the inverse problem of identifying the distributed growth law in the initial value problemfrom the noisy data One can reformulate the above problem as an (ill-posed) operator equation with It is proved in [7], that is positive type (sectorial) and spectrum of is the singleton set Further it is proved in [5] that satisfies Assumption 1 and that provided and Now since we havewhere This shows the source condition (10) is satisfied. For our computation we have taken and In Table 1, we present the relative error and α values using a new method (51) and adaptive method considered in [16] for different values of δ and Furthermore, we provide computational time (CT) for both the methods mentioned above. The relative error obtained for our a new method (51) is lesser than that the adaptive method in [16] for various δ values. As the relative error decreases the accuracy of reconstruction increases. The solutions obtained for different δ values () for are shown in Figure 1, Figure 2 and Figure 3, respectively, and for and are shown in Figure 4, Figure 5 and Figure 6, respectively. The exact and noisy data are shown in subfigure (a) of these figures and the computed solution is shown in subfigure(b) (C.S-A priori denotes the figure corresponding to the method (51)). The computed solution for the new method is closer to the actual solution.