1. Introduction
The approximation theory of functions is a classical theory of basic mathematics and computational mathematics, and width theory plays a very important role in approximation theory. With the gradual development of modern mathematics and science, the system of width theory has also been improved, which has greatly promoted the research of algorithms and computational complexity. Different types of widths correspond to different calculation methods, and then result in different errors. The different definitions of algorithm errors and costs lead to different computational models. The most common models are the worst case setting, probabilistic setting, and average-case setting. Temlyakov [
1] calculated the bounds of approximation of functions with a bounded mixed derivative. Maiorov [
2,
3,
4] gave the definition of probabilistic Kolmogorov and linear
-widths and obtained the sharp bounds of probabilistic Kolmogorov
-widths of Sobolev space
in
by using discretization. Fang and Ye [
5,
6] estimated the exact order of linear
N-widths in the probabilistic setting and average-case setting of finite dimensional space. Chen and Fang [
7,
8] discussed probabilistic Kolmogorov
-widths and probabilistic linear
-widths of the multivariate Sobolev space
with a mixed derivative, and they obtained the sharp bounds of
p-average Kolmogorov and linear
N-widths of
. Tan et al. [
9] gave the definition of probabilistic Gel’fand
-width and obtained the sharp bounds of probabilistic Gel’fand
-width of Sobolev space
. Liu et al. [
10] gave the definition of
p-average Gel’fand
N-width and obtained the sharp bounds of
p-average Gel’fand
N-widths of Sobolev space
and
. Dai and Wang [
11] obtained the sharp bounds of probabilistic linear
-widths and
p-average linear
N-widths of finite dimensional space with a diagonal matrix. Wang [
12,
13] estimated the sharp bounds of probabilistic linear
-widths and
p-average linear
N-widths of weighted Sobolev spaces on the ball and Sobolev spaces on compact two-point homogeneous spaces.
Let us recall some definitions of
N-widths, which can be found from the book of Pinkus [
14].
Let
W be a bounded subset of a normed linear space
X with norm
, and
be a
N-dimensional subspace of
X. The following quantity is called the deviation of
W to
:
where
. It shows how well the “worst” elements of
W can be approximated by
. And the Kolmogorov
N-width of
W in
X is defined as follows:
where the leftmost infimum is taken over all
N-dimensional linear subspaces of
X.
Next, let
T be a linear operator from
X to
X. The linear distance of the image
from the set
W is defined as follows:
and the linear
N-width of
W in
X is defined as follows:
where the infimum is taken over all linear operators
whose rank is at most
N.
Now we give the definition of probabilistic
-widths and
p-average
N-widths from the article of Maiorov [
2,
3,
4].
Definition 1. Let W be a bounded subset of normed linear space . Assume that W contains a Borel field B consisting of open subsets of W and is equipped with a probability measure μ, i.e., μ is a σ-additive nonnegative function on B, and satisfies the condition that . For any , the probabilistic Kolmogorov -width and probabilistic linear -width of W in X are defined as follows:where runs through all possible subsets in B, which satisfies the condition that . Definition 2. Let W, X and μ be the same to Definition 1. Given , the p-average Kolmogorov N-width and p-average linear N-width are defined, respectively, by It can be seen from the definition that N-widths are defined by the errors generated by the “worst” elements of the functions class during the approximation process in the worst case setting. For example, the classical Kolmogorov N-widths of functional classes are defined by the optimal errors generated by the approximation of the “worst” element in the set by a finite dimensional subspace. To satisfy the demands of practical applications and theoretical analysis, the concepts of N-widths in the probabilistic and average-case setting are introduced. The sharp bounds of those widths are often used to solve the optimal solution of numerical problems. Like classical N-widths, probabilistic -widths reflect the best approximation of functional classes. From the definitions, we know that it needs to delete some functions with the “worst” properties before defining N-widths of functional classes in the probabilistic setting, and these widths are still defined by the “worst” elements of the remaining functions. Therefore, although the probabilistic -widths can allow the algorithm to generate “errors” within a given range, it does not reflect the overall optimal approximation situation. The N-widths in the average-case setting are defined by the integral of the errors under a given measure, which give the average approximation degree of a function class under a given probability measure. They reflect the optimal approximation degree of most elements in spaces, and more profoundly reflect the essential characteristics of the structure of the functional classes.
Next, we will provide two asymptotic relationships. Let and be two positive functions of x. If there is a positive constant , such that for all x from the domain of the functions a and b, then we write or . If and , then we write .
2. Main Results
In this article, we will discuss probabilistic Kolmogorov and linear -widths. Then, we will estimate the sharp bounds of p-average Kolmogorov and linear N-widths by using the results of probabilistic Kolmogorov and linear -widths. First, we introduce the concept of multivariate Sobolev space , where .
Let , , , . We write , , .
Assume
is a classical Lebesgue square integrable space. For any
, this space is a Hilbert space with the inner product
For
, the Fourier series of
x is defined as follows:
where
.
For any
, we define the Wyel
-derivative for
as follows:
where
,
.
Given the finite subset
A of
, the multivariate Sobolev
with common smoothness is defined by
From Equation (
7), we need to give the definition of the common Weyl-derivative as follows:
where
. We can know that the Sobolev space
is a Hilbert space with the inner
and with the norm
.
Our results of the Sobolev space
with common smoothness can be a generalization of the sharp bounds of
N-widths in the probabilistic and average setting of Sobolev spaces with smoothness. For example, if
, then
. Space
is a Sobolev space with a mixed derivative, and the related conclusions can be found in papers [
7,
8].
We denote by
A and
B any two subsets of
, and we denote that
Let
be the convex hull of a set
A,
, and
be the set of interior points of
. We write
,
,
,
. In the research process of this article, we always assume that
and
.
Now, we give the definition of the space
:
where
is the infinite vector space with the norm for any
:
For any
, let
be the norm of
.
Next, we equip a Gaussian measure for
. Let
be a Gaussian measure whose mean value is 0 and whose correlation operator is
which has eigenfunctions
and eigenvalues
, that is,
Let
be any orthogonal system of functions in
,
,
, and
be an arbitrary Borel subset of
. Then, the Gaussian measure
on the cylindrical subsets in the space
:
is given by
More results and research of Gaussian measures can be found in paper [
15,
16,
17].
The aim of this paper is to determine the asymptotic order of probabilistic Kolmogorov and linear -widths as well as p-average Kolmogorov and linear N-widths of the multivariate Sobolev space with common smoothness. The main results are as follows:
Theorem 1. Assume that , , , , . Let A be a finite subset of and . Note . Then, Theorem 2. Assume that , , , , . Let A be a finite subset of and . Note . Then, Theorem 3. Assume that , , , , . Let A be a finite subset of and , . Then, Theorem 4. Assume that , , , , . Let A be a finite subset of and , . Then, 3. Discretization
In order to prove Theorems 1 and 2, we use the discretization method, which is based on the reduction of the calculation of the probabilistic widths of a given class to the computation of the widths of a finite-dimensional set equipped with the standard Gaussian measure. Before we use the discretization, we need the definitions, and cite some results on the probabilistic widths of finite-dimensional spaces. Let
be the
m-dimensional normed space of vectors
, with norm
Consider in
the standard Gaussian measure, which is defined as
where
G is any Borel subset in
. Obviously,
.
First, we introduce some results of probabilistic
-widths of finite space. These results can be found from papers of Maiorov, Chen, Fang, and Ye [
2,
3,
4,
5,
6,
7,
8].
Lemma 1 (Maiorov, Chen, and Fang [
3,
4,
7])
. Let , and . Then, Lemma 2 (Maiorov, Fang, and Ye [
2,
5,
6])
. Let , and . Then, Lemma 3 (Maiorov [
3])
. For , there is a positive , such thatFor and any , there exists a positive constant , which depends only on the q, such that To establish the discretization theorem, we introduce some notations and lemmas. It is convenient in many cases to split the Fourier series of a function into the sum of diadic blocks. We associate every vector
whose coordinates are natural numbers with the set
And we let
be the “block” of the Fourier series for
, denoted by
After introducing these necessary concepts, we have
Lemma 4 (Galeev [
18])
. Let . Then, the trigonometric polynomial space and are isomorphic under the following mapping:where , , .
For natural numbers
l and
k, we define
where
. We can know
, and
for
.
Let . We can obtain that . And we define .
From ([
7]), for any
,
. So,
From the definition of
, we know
Therefore, for any
, we have
That is,
We consider a mapping:
It is not difficult to see that
is a isomorphic mapping. From Equation (
9), we know that
.
By
([
7]), we obtain
Therefore, from Equation (
25), we have
Let
. Therefore,
Based on the above description, we establish the discretization theorem. The following theorems reflect the upper bounds of Theorems 1 and 2.
Theorem 5. Let , , , , , A satisfy the condition of Theorem 1. Assume that the sequences of numbers and satisfy the condition , and . Then Proof. From Definition 1, there would be a subspace
of
such that
and
where
.
From Equation (
27), there is a constant
independent of
l and
k, such that
From Equations (
29) and (
30), the definition of
and
v,
Let
,
, where
is the direct sum of
. Therefore,
and
Consequently, by Definition 1, we have
which completes the proof of Theorem 5. □
To estimate the upper bound of Theorem 1, we need the following lemmas.
Lemma 5 (Romanyuk [
19])
. Assume that the set A satisfies the condition of Theorem 1, thenwhere . From Lemma 5, we have
Lemma 6. For any , , , letwhere is the integer part of a. Then, we can choose c, such that , . We assume that in Lemma 6, the constant satisfies .
To establish the discretization of the lower bound of Theorem 1, we also need the following concepts. Let
where the constants
and
k are pending. Then,
Therefore,
,
.
Let
. Consider the mapping:
Then for any
, by using the method of the proof of Equation (
26), we can obtain
Theorem 6. Let , , , , , A satisfy the condition of Theorem 1. Then Proof. From Definition 1, there is a subspace
, such that
and
where
.
Let
, where
, such that
Equation (
35) can be obtained by Equation (
32); therefore,
Due to
and Definition 1, we have
That is,
. □
Theorem 7. Let , , , , , A satisfy the condition of Theorem 1. Assume that the sequences of numbers and satisfy the condition , and . Then, Proof. From Definition 1, there would be a linear operator
of
into itself, such that
and
where
.
From Equation (
27), there is a constant
independent of
l and
k, such that
From Equations (
37) and (
38), the definition of
and
v,
Let
,
, where
is the direct sum of
. Therefore,
and
Consequently, by Definition 1, we have
which completes the proof of Theorem 7. □
Theorem 8. Let , , , , , A satisfy the condition of Theorem 1. Then, Proof. From Definition 1, there is a linear operator
, such that
and
where
.
Let
, where
, such that
Equation (
41) can be obtained by Equation (
32). Let
Therefore,
Due to
and Definition 1, we have
That is, . □
4. Proof of Main Results
Now we prove Theorem 1 by using Theorems 5 and 6 and Lemma 1, and prove Theorem 2 by using Theorems 7 and 8 and Lemma 2. And then, we prove Theorems 3 and 4 by using results of Theorems 1 and 2. Assume that
satisfies the condition of Lemma 5 and assume that
satisfies the condition
. Let
Therefore,
.
Proof of Theorem 1. From Theorem 5, Lemma 1, for
, we have
First, we calculate
:
Split term for
:
where
is carried out over
k for
, and
is carried out over
k for
. Therefore,
and
Therefore,
So, we obtain
Due to
, we have
Secondly, we calculate
:
By using the method of the proof of Equation (
42), we can obtain
Therefore,
Due to
, we have
Finally, we calculate
:
By using the method of the proof of Equation (
42), we can obtain
Therefore,
Summarily, if
,
If
, from Theorem 5, Lemma 1, and the definition of
, we have
First, we calculate
:
By using the method of the proof of Equation (
42), we can obtain
Therefore,
Due to
, we obtain
Secondly, we calculate
:
By using the method of the proof of Equation (
42), we can obtain
Therefore,
Due to
, we obtain
Finally, we calculate
:
By using the method of the proof of Equation (
42), we can obtain
Therefore,
Due to
, we obtain
Summarily, if
,
Now we begin to prove the lower bound of Theorem 1. If
, from Theorem 6 and Lemma 1, we have
That is,
which completes the proof of Theorem 1. □
Proof of Theorem 2. First, we prove the upper bound of Theorem 2. From Lemma 2, if
,
and
have the same sharp bounds. So, we only need to prove the upper bound if
. From Theorem 7 and Lemma 2, we obtain
It is obvious to see that
. Therefore,
Now, we calculate
:
By using the method of the proof of Equation (
42), we can obtain
Therefore,
Next, we calculate
:
Summarily, if
,
Finally, we prove the lower bound of Theorem 2. From Lemma 2, we only need to prove the lower bound of Theorem 2 if
. From Theorem 8 and Lemma 2, we have
That is, if we note
, then
which completes the proof of Theorem 2. □
Proof of Theorem 3. We consider the decreasing sequence of sets
, such that
for each
k and
. Then,
. From Theorem 1, there would be a subspace
, such that
and
Therefore, from Definition 2:
Due to the astringency of
, we have
Next, we prove the lower bound of Theorem 3. We consider the set
Then,
. If not, we have
So, we obtain contradictions. Therefore,
That is,
□
Proof of Theorem 4. We consider the decreasing sequence of sets
, such that
for each
k and
. Then,
. From Theorem 2, there would be a linear operator
from
into itself, such that
and
Therefore, from Definition 2,
Due to the astringency of
and
, we have
Next, we prove the lower bound of Theorem 4. We consider the set
Then,
. If not, we have
So, we obtain contradictions. Therefore,
That is,
□
In summary, the proof of main results are completed.
5. Summary
In this article, we have obtained the sharp bounds of Kolmogorov and linear N-widths in the probabilistic and average setting of the Sobolev space in the -norm. In the process of calculating, we use discretization. Discretization means that we can transform function space into finite-dimensional space. It can reduce the calculation of the probabilistic -widths. The sharp bounds of the p-average N-widths should be obtained by the sharp bounds of the probabilistic -widths. These results can be used to the research of algorithms and computational complexity. And these results may play important roles of the research of approximation theory of Sobolev spaces.
On the other hand, other related theories have not yet been studied. For example, we can study the sharp bounds of probabilistic Gel’fand -widths and p-average Gel’fand N-widths of in the -norm and -norm. The above issues can be studied later.