1. Introduction
For
, let
be the one-parameter Wiener space and let
denote the class of all Wiener measurable subsets of
. Let
m denote Wiener measure. Then, the space
is complete, and we denote the Wiener integral of a Wiener integrable functional
F by
Let be the space of all complex-valued continuous functions defined on which vanishes at and whose real and imaginary parts are elements of .
In [
1], Lee studied an integral transform of analytic functionals on abstract Wiener spaces
For some parameters
and
and for certain classes of functionals, the Fourier–Wiener transform, the modified Fourier–Wiener transform, the analytic Fourier–Feynman transform and the Gauss transform are popular examples of the integral transform defined by (
1) above (see [
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12]). Researchers have studied some theories of integral transform for functionals on function space. Recently, the integral transform is generalized by some methods in various papers. One of them uses the concept of Gaussian process instead of the ordinary process. For a function
h on
, the Gaussian process is defined by the formula
where
the Paley–Wiener–Zygmund (PWZ) stochastic integral. Many mathematician use this process to generalize the integral. As representative examples, the generalized integral transforms
and
are studied in [
13,
14,
15]. In fact, if
and
are identically 1 on
, then Equations (
2) and (
3) reduce to Equation (
1).
Another method is using the operators on
K. Let
S and
R be bounded linear operators on
K. In [
6,
16], the authors used this operators to generalize the integral transforms. A more generalized form is given by
If
R is a constant operator and
for some function
h, then Equation (
4) reduces to Equation (
2), and hence it reduces to Equation (
1) again. In previous studies, many relationships among the integral transform, the convolution and the first variation have been obtained. However, most of the results consist of fixed parameters.
In this paper, we use the both concepts, the Gaussian process and the operator, to define a more generalized integral transform, a generalized convolution product and a generalized first variation of functionals on function space. We then give some necessary and sufficiently conditions for holding some relationships between the generalized integral transforms and the generalized convolution products, and between the generalized integral transforms and the generalized first variations. In addition, some examples are given to illustrate usefulness for our formulas and results. By choosing the kernel functions and operators, all results and formulas in previous papers are corollaries of our results and formulas in this paper.
2. Definitions and Preliminaries
We first list some definitions and properties needed to understand this paper.
A subset
B of
is called scale-invariant measurable if
is
-measurable for all
, and a scale-invariant measurable set
N is called a scale-invariant null set provided
for all
. A property that holds except on a scale-invariant null set is said to hold scale-invariant almost everywhere (s-a.e.) [
17]. For
and
, let
denote the Paley–Wiener–Zygmund (PWZ) stochastic integral. Then, we have the following assertions.
- (i)
For each , exists for a.e. .
- (ii)
If is a function of bounded variation on , equals the Riemann–Stieltjes integral for s-a.e. .
- (iii)
The PWZ stochastic integral has the expected linearity property.
- (iv)
The PWZ stochastic integral is a Gaussian process with mean 0 and variance .
For a more detailed study of the PWZ stochastic integral, see [
4,
5,
7,
8,
9,
11,
12,
13,
14,
15,
18].
Then,
is the Hilbert space with the inner product
where
for
. Furthermore, we note that
and
is one example of the abstract Wiener space [
1,
16,
19,
20]. For
and
with
,
is a well-defined Gaussian random variable with mean 0 and variance
, where
is the complex bilinear form on
.
The following is a well-known integration formula which is used several times in this paper. For each
with
,
For each
, let
These functionals are called the exponential functionals on
. It is a well-known fact that the class
is a fundamental set in
. Thus, there is a countable dense
which is dense in
. Thus, we have that, for each
,
in the
-sense, where
is a sequence of constants.
Let
be the class of all bounded linear operators on
K. Then, for each
and
,
where
is the adjoint operator of
S, see [
16,
19,
21]. We state the conditions for the function
h to obtain mathematically consistency as follows:
- (i)
For each
,
where
for some
because, although
,
may not be an element of
for
.
- (ii)
Then, h is in (and hence ). However, may not be a Gaussian process. A condition for h is needed. Let h be an element of such that , where is the Lebesgue measure. Then, we have and is a Gaussian process.
- (iii)
For each
and
,
is stochastically continuous but it is not continuous, namely
may not element of
. However, if
h is a function of bounded variation on
, the Gaussian process
is continuous and hence
is well-defined for all
. Since for
with
,
, we have that
- (iv)
Let
3. Generalization of the Integral Transform with Related Topics
We start this section by giving definition of generalized integral transform, generalized convolution product and the generalized first variation of functionals on K.
Definition 1. Let be an element of and let F and G be functionals on K. Let . Then, the generalized integral transform of F, a generalized convolution product of F and G, and a generalized first variation of F with respect to and are defined by the formulasandfor if they exist. Remark 1.
- (1)
When on , the generalized integral transform is the Fourier–Gauss transform [16]. - (2)
When S and R are the constant operators, the generalized integral transform is a generalized integral transform used in [14,15]. In particular, if on , then is the integral transform used in [5,6,8,10,11,13,22]. - (3)
When and on , is the convolution product used in [11].
We next state some notations used in this paper. For
and
, let
where
for each
. Furthermore, we have the symmetric property for
.
In Theorem 1, we obtain the existence of generalized integral transform, generalized convolution product and generalized first variation of functionals in . In addition, we show that they are elements of .
Theorem 1. Let be elements of and let . Let and be elements of and let . In addition, let for . Then, the generalized integral transform of , the generalized convolution product of and and the generalized first variation with respect to and exist, belong to and are given by the formulasandfor . Proof. First, using Equations (
5), (1) and (
8), it follows that, for all
, we have
Finally, by using Equations (
12) and (
13) is obtained. We next use Equations (
5), (
8) and (
14) to obtain the following calculation
Since
for all
, we now note that
and
for all
. Hence, we can obtain Equation (
14) as desired. Finally, we use Equations (
8) and (
11) to establish Equation (
15) as follows:
We now note that
which establishes Equation (
15) as desired. □
4. Some Relationships with the Generalized Convolution Products.
In this section, we obtain some relationships between the generalized integral transform and the generalized convolution product of functionals in
. In the first theorem in
Section 4, we give a formula for the generalized integral transforms of functionals in
. To establish some relationships, the following lemma is needed.
Lemma 1. Let and let . Then, for each , Proof. Using the following fact
and Equation (
12) repeatedly, we have
which complete the proof of Lemma 1. □
Theorem 2. Let and be elements of and let and be elements of . In addition, let be an element of . Then,for . Proof. Applying Theorem 1 once more,
Finally, using Equation (
16) in Lemma 1, we complete the proof of Theorem 2 as desired. □
Equations (
18) and (
19) in Theorem 3 are the commutative of the generalized integral transform and the Fubini theorem with respect to the generalized integral transform, respectively.
Theorem 3. Let and be elements of and let and be elements of . In addition, let be an element of . Then,if and only if Furthermore,if and only if Proof. Using Equation (
17) twice, we have
and
Using these facts and Equation (
13), we can establish Equations (
18) and (
19). □
From Theorems 2 and 3, we can establish the n-dimensional version for the generalized integral transform.
Corollary 1. Let and be elements of and let be an element of . In addition, let be an element of . Then, In our next theorem, we show that our generalized convolution product is commutative.
Theorem 4. Let and D be elements of and let . Let and be elements of . Then,if and only if Proof. The proof of Theorem 4 is a straightforward application of Theorem 1. □
In Theorem 5, we give a necessary and sufficient condition for holding a relationship between the generalized integral transform and the generalized convolution product.
Theorem 5. For , let , and, for , let . In addition, for , let . Then,if and only if the following equations hold Proof. To complete the proof of Theorem 5, we first calculate the left hand side of Equation (
21). From Equation (
14) in Theorem 1, we have
Using Equations (
13), (
12), (
16) and (
22), we have
We next calculate the left hand side of Equation (
21). From Equations (
12) and (
13) twice, we have
and
We now use Equations (
14), (
16), (
23) and (
24) repeatedly to obtain the following calculation
Hence, we complete the proof of Theorem 5 as desired. □
Corollary 2. The following results and formulas stated bellow easily from Theorem 5.
- (1)
Let S and R be elements of , and, for , let . In addition, for , let . Then, if and only if the following equations hold - (2)
For , let and . In addition, for , let . Then, if and only if the following equations hold
5. Some Relationships with the Generalized First Variations
In this section, we establish some formulas involving the generalized first variation. We next obtain a generalized Cameron–Storvick theorem for the generalized first variation and use this to apply for the generalized integral transform.
Theorem 6. Let and . Let with . Then,if and only if and , where . Proof. First, using Equations (
5), (
12), (
13) and (
29), we have
On the other hands, using Equations (
11)–(
13), we have
Hence, Equation (
25) holds if and only if
and
□
To establish a generalized Cameron–Storvick theorem for the generalized first variation, we need two lemmas with respect to the translation theorem on Wiener space.
Lemma 2. (Translation Theorem 1)
Let F be a integrable functional on and let . Then, In [
23], the authors used Equation (
26) to establish Equation (
28), which is a generalized translation theorem. The main key in their proof is the change of kernel for the Gaussian process, i.e.
where
and
for given
.
The following lemma is said to be the translation theorem via the Gaussian process on Wiener space.
Lemma 3 (Translation Theorem 2).
Let . Let and let be a integrable functional on . Let In our next theorem, we establish the generalized Cameron–Storvick theorem for the generalized first variation.
Theorem 7. Let be given. Let and . In addition, let and . Then, Proof. First, by using Equation (
11) and the dominated convergence theorem, we have
Now, let
. Using the key (
27) used in [
23], we have
where
and
. This means that
We next apply the translation theorem to the functional
instead of
F in Lemma 2 to proceed the following formula
Since , we complete the proof of Theorem 7 as desired. □
In the last theorem in this paper, we use Equation (
29) to give an integration formula involving the generalized first variation and the generalized integral transform. This formula tells us that we can calculate the Wiener integral of generalized first variation for generalized integral transform directly without calculations of them.
Theorem 8. Let and let . In addition, let be as in Theorem 7. Then, Proof. Applying Equation (
29) to the functional
instead of
, we have
Now, using Equations (
8) and (
13), it becomes that
The following integration formula
and Equation (
12) yield that
Finally, by using Equation (
16) in Lemma 1, we establish Equation (
30) as desired. □
6. Application
We finish this paper by giving some examples to illustrate the usefulness of our results and formulas.
We first give a simple example used in the stack exchange and the signal process. For , let . Then, the adjoint is given by the formula .
Example 1. Let and let and on . Then, . In addition, we have This means that on and hence Thus, we obtain that We give two examples in the quantum mechanics. To do this, we consider useful operators used in quantum mechanics. We consider two cases. However, various cases can be applied in appropriate methods as examples.
Case 1: Multiplication operator.
In the next examples, we consider the multiplication operator
, which plays a role in physics (quantum theories) (see [
21]). Before do this, we introduce some observations to proceed obtaining examples. Let
such that
for all
. In addition, for
on
, we define a multiplication operator
by
Then, we have
and
Hence, Equation (
31) holds. In addition, one can easily check that
for all
. Note that the expected value or corresponding mean value is
where
x is the state function of a particle in quantum mechanics and
is the probability that the particle will be found in
.
In the first and second examples, we give some formula with respect to the multiplication operator .
Example 2. Let and let and on . Then, . In addition, we haveand This means that and on and hence Thus, we obtain that Example 3. Let and let and on . Then, . In addition, we haveand This means that and on and hence Case 2: Quantum mechanics operators.
In the next examples, we consider some linear operators which are used to explain the solution of the diffusion equation and the Schrôdinger equation (see [
24]).
Let
be the linear operator defined by
Then, the adjoint operator
of
S is given by the formula
and the linear operator
is given by the formula
In addition,
A is self-adjoint on
and so
for all
. Hence,
A is a positive definite operator, i.e.,
for all
. This means that the orthonormal eigenfunctions
of
A are given by
with corresponding eigenvalues
given by
Furthermore, it can be shown that
is a basis of
and so
is a basis of Ł2, and that
A is a trace class operator and so
S is a Hilbert–Schmidt operator on
. In fact, the trace of
A is given by
. By using the concept of
m-lifting on abstract Wiener space, the operators
S and
A can be extended on
(see [
19,
25]).
We now give formulas with respect to the operators S and A, respectively.
Example 4. Let S be given by Equation (33) and let and on . Then, . In addition, we haveand This means that and on and hence Thus, we obtain that Example 5. Let and let and on . Then, . In addition, we haveand This means that and on and hence Thus, we obtain that We now give an example with respect to Theorem 8.
Example 6. Let and , as used in the examples above. Let and let on . Furthermore, let on and let . Then, we have and on . Furthermore, we haveand Hence, by using Equation (30) in Theorem 8, we can conclude that 7. Conclusions
In
Section 3 and
Section 4, we establish some fundamental formulas for the generalized integral transform, the generalized convolution product and the generalized first variation involving the generalized Cameron–Storvick theorem. As shown in Examples 2, 4 and 6, various applications are established by choosing the kernel functions and operators. The results and formulas are more generalized forms than those in previous papers. From these, we can conclude that various examples can also be explained very easily.