On generalizations of $p$-sets and their applications
Heng Zhou, Zhiqiang Xu

TL;DR
This paper introduces generalized $p$-sets that are more flexible than traditional $p$-sets, providing new constructions with potential applications in numerical analysis and related fields, assuming Goldbach's conjecture.
Contribution
The paper develops generalized $p$-sets $\\mathcal{P}_{d,p}^{{\mathbf a},\epsilon}$ and constructs point sets ${\mathcal L}_{p,q}$ under Goldbach's conjecture, overcoming limitations of classical $p$-sets.
Findings
Upper bounds for exponential sums over the new $p$-sets.
Construction of point sets with arbitrary even cardinalities.
Potential applications in numerical integration, compressed sensing, and UQ.
Abstract
The -set, which is in a simple analytic form, is well distributed in unit cubes. The well-known Weil's exponential sum theorem presents an upper bound of the exponential sum over the -set. Based on the result, one shows that the -set performs well in numerical integration, in compressed sensing as well as in UQ. However, -set is somewhat rigid since the cardinality of the -set is a prime and the set only depends on the prime number . The purpose of this paper is to present generalizations of -sets, say , which is more flexible. Particularly, when a prime number is given, we have many different choices of the new -sets. Under the assumption that Goldbach conjecture holds, for any even number , we present a point set, say , with cardinality by combining two different new -sets, which…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Mathematical Approximation and Integration · Digital Image Processing Techniques
On generalizations of -sets and their applications
Heng Zhou
School of Sciences, Tianjin Polytechnic University, Tianjin 300160, China
E-mail address: [email protected]
and
Zhiqiang Xu
LSEC, Institute of Computational Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100190, China
E-mail address: [email protected]
Heng Zhou was supported by the National Natural Science Foundation of China (No. 61602341); Zhiqiang Xu was supported by NSFC grant (11422113, 91630203, 11331012) and by National Basic Research Program of China (973 Program 2015CB856000).
Abstract: The -set, which is in a simple analytic form, is well distributed in unit cubes. The well-known Weil’s exponential sum theorem presents an upper bound of the exponential sum over the -set. Based on the result, one shows that the -set performs well in numerical integration, in compressed sensing as well as in UQ. However, -set is somewhat rigid since the cardinality of the -set is a prime and the set only depends on the prime number . The purpose of this paper is to present generalizations of -sets, say , which is more flexible. Particularly, when a prime number is given, we have many different choices of the new -sets. Under the assumption that Goldbach conjecture holds, for any even number , we present a point set, say , with cardinality by combining two different new -sets, which overcomes a major bottleneck of the -set. We also present the upper bounds of the exponential sums over and , which imply these sets have many potential applications.
Key words and phrases -set; Deterministic sampling; Numerical integral; Exponential sum
AMS Subject Classification 2000 11K38, 65C05, 11L05, 41A10, 65D32
1. Introduction
1.1. -set
Let be a prime number. We consider the point set
[TABLE]
where
[TABLE]
and is the fractional part of for a nonnegative real number . The point set is called -set and was introduced by Korobov [5] and Hua-Wang [4]. Recently, -set attracts much attention since its advantage in numerical integration [1], in the recovery of sparse trigonometric polynomials [12] and in the UQ [13]. In [1], Dick presents a numerical integration formula based on with showing the error bound of the formula depends only polynomially on the dimension . In [12], Xu uses to construct the deterministic sampling points of sparse trigonometric polynomials and show the sampling matrix corresponding to has the almost optimal coherence. And hence, has a good performance for the recovery of sparse trigonometric polynomials.
1.2. Extensions of -set: and
The -set is in a simple analytic form and hence it is easy to be generated by computer. However, the -set is somewhat rigid with the point set only depending on a prime number . If the function values at some points in -set are not easy to be obtained, one has to change the prime number to obtain a new point set which has the different cardinality with the previous one. Hence, in practical application, it will be better that one has many different choices. We next introduce a generalization of -set.
Let
[TABLE]
Suppose that and . We set
[TABLE]
where
[TABLE]
and . We call as the -set associating with the parameter and . If we take and , then is reduced to the classical -set.
The -set associating with the parameters is more flexible. Given the prime number , one can generate various point sets by changing the parameters and with presenting an option set when the cardinality is given.
Note that the cardinality of both and is prime. Since the distance between adjacent prime can be very large, the cardinality of -set does not change smoothly. Using the set , we next present a set with the cardinality being odd number. Suppose that is given. The Goldbach conjecture, which is one of the best-known unsolved problem in number theory, says that can be written as the sum of two primes, i.e., where and are prime numbers. One has verified the conjecture up to which is enough for practical application. We next suppose that with and being prime numbers. We set
[TABLE]
where and are the -sets that we have defined above and . We call the -set. As shown later, provided . We can choose and so that . Hence, under the assumption of Goldbach conjecture, for any odd number, says , there exist so that .
We would like to mention the following point sets with cardinality [5, 4] :
[TABLE]
The weighted star discrepancy of and is given in [2]. Using a similar method with above, we can generalize and to and , respectively. We will introduce it in Section 2.3 in detail.
1.3. Organization
In Section 2, we present the upper bounds of the exponential sums over and . Particularly, we present the condition under which and also prove that when . We furthermore consider the generalization of the point sets and and present the upper bounds of exponential sums over the new sets. The results in Section 2 show that the point sets presented in this paper have many potential applications in various areas. In Section 3, we choose as a deterministic sampling set for the recovery of sparse trigonometric polynomials and then show their performance.
2. The exponential sums over and
The aim of this section is to present the exponential sums over and . To this end, we first introduce the well-known Weil’s formula, which plays a key role in our proof.
Theorem 2.1**.**
[11]** Suppose that is a prime number. Suppose with and there is a , satisfying . Then
[TABLE]
2.1. The exponential sum over
Recall that
[TABLE]
and
[TABLE]
where , and . Note that . We next show the exponential sum formula over .
Theorem 2.2**.**
For any and , we have
[TABLE]
Proof.
Set
[TABLE]
where . We set . Then and we have . According to Theorem 2.1, we obtain that
[TABLE]
∎
2.2. The exponential sum over
To this end, we consider the cardinality of . A simple observation is that . We would like to present the condition under which . We first consider the case where .
Theorem 2.3**.**
Suppose that and are two distinct prime numbers. Then .
Proof.
According to (2), to this end, we just need show that
[TABLE]
We prove it by contradiction. Assume that , and then there exists and so that , . Particularly, we have , which is equivalent to . Since and are different prime numbers, and , we have and , which means and hence . A contradiction. ∎
We next consider the case where , i.e., . For the case where , we have
Theorem 2.4**.**
Suppose that is a fixed vector and , .
- (1)
* if and only if there exists such that*
[TABLE] 2. (2)
If , then .
Proof.
(1) We first suppose that there exists such that for . Recall that
[TABLE]
where
[TABLE]
For any , we take . Then
[TABLE]
which implies that
[TABLE]
Here we use which follows from .
Since is a prime number, there exists so that . Then we have . Then, similarly, for any ,
[TABLE]
which implies that
[TABLE]
Then we arrive at
[TABLE]
We next suppose that . Then there exist so that , i.e.,
[TABLE]
[TABLE]
[TABLE]
[TABLE]
We set , where so that . Then (5) implies that . Combining (5) and (6), we have which implies that . Similarly, we can obtain that for .
(2) We prove it by contradiction. Assume that , and then there exist so that . Similarly with the above proof, we can find a so that for . It leads to by (1) of Theorem 2.4, which is impossible by the assumption in (2). ∎
We next consider the case where .
Theorem 2.5**.**
Suppose that with . Set
[TABLE]
and
[TABLE]
Then the followings hold.
- (1)
* if and only if there exists a so that*
[TABLE]
where with being given. 2. (2)
Assume that where . If then we have
[TABLE]
If , then , where
[TABLE] 3. (3)
Assume that , and . If , then .
Proof.
(1) We assume that (7) holds. Take
[TABLE]
Noting that provided , we have and . Theorem 2.4 implies that and hence .
We next assume that which is equivalent to that there exists a permutation of so that
[TABLE]
This is equivalent to
[TABLE]
and
[TABLE]
for . Since , by (9) we have
[TABLE]
Set . Using the same argument with the one in Theorem 2.4 we have , . Combining (10) for and (11) we have
[TABLE]
Without loss of generality, we can assume and and then
[TABLE]
which implies since and .
(2) We first assume that which implies provided . Take
[TABLE]
Noting that provided , we have and . The (2) of Theorem 2.4 implies that . We next consider the case where . Suppose that and have a common nonzero point. Then, there exist so that
[TABLE]
Note that when and . The (12) implies that
[TABLE]
[TABLE]
Without loss of generality, we can assume and . By (14), we have
[TABLE]
Taking in (13), we have where satisfies . Since , we have . Set . Then satisfies
[TABLE]
Each nonzero point in corresponds to a solution to
[TABLE]
Note that has at most solutions. Hence,
[TABLE]
(3) We prove it by contradiction. Assume that , and then there exist so that . Particularly, we have
[TABLE]
[TABLE]
Without loss of generality, we can assume and . By (17), we have
[TABLE]
By (16) with , we have
[TABLE]
according to . By (18), we have , which implies that or . This is impossible by the assumption.
∎
In the following, we choose the appropriate vectors so that . We now state the inequalities for exponential sums over , which is the main result of this subsection.
Theorem 2.6**.**
Suppose and are odd prime numbers and set . Recall that
[TABLE]
We assume that . Then, for any and , we have
[TABLE]
Proof.
We first consider the case where . We have
[TABLE]
Recall that
[TABLE]
Then
[TABLE]
Here, in the last inequality, we use Theorem 2.2. We next consider the case where . When , . Then we have
[TABLE]
∎
2.3. The exponential sums over and
Suppose that and . We set
[TABLE]
[TABLE]
and
[TABLE]
[TABLE]
The and can be considered as the generalization of the -sets given in . Based on the Lemma 5 and Lemma 6 in [2], we can obtain the following inequalities for exponential sums over and .
Theorem 2.7**.**
Suppose that and . Then, for any and , we have
[TABLE]
Proof.
Set
[TABLE]
where . We set . Then and we have . According to Lemma 5 in [2], we have
[TABLE]
∎
Theorem 2.8**.**
Suppose that . Then, for any and , we have
[TABLE]
Proof.
Set
[TABLE]
and . We set . Then and we have . Using Lemma 6 in [2], we have
[TABLE]
∎
3. The applications of and
Based on the exponential sum formula in Section 2, the new point sets are useful in numerical integration [8, 1], in UQ [13] and in the recovery of sparse trigonometric polynomials [12]. We just state the results for the recovery of sparse trigonometric polynomials in detail.
We start with some notations which go back to [12]. Set
[TABLE]
Note that is a linear space with the dimension . For
[TABLE]
we set which is the support of the sequence of coefficients , and set
[TABLE]
where denotes the space of all trigonometric polynomials whose coefficients are supported on . When , we call the trigonometric polynomials in as -sparse trigonometric polynomials.
The recovery of sparse trigonometric polynomials is an active topic recently. The main aim of this research topic is to design a sampling set so that one can recover from [12, 9, 6]. We state the problem as follows. Assume the sampling set is . Then our aim is to solve the following programming:
[TABLE]
Denote by the sampling matrix with entries
[TABLE]
Let denote a column of with . A simple observation is that . Set
[TABLE]
which is called the mutual incoherence of the matrix . Theorem 2.5 in [6] shows that if then the Orthogonal Matching Pursuit Algorithm (OMP) and the Basis Pursuit Algorithm (BP) can recover any -sparse trigonometric polynomials in . Therefore, our aim is to choose the sampling set so that is small and hence OMP and BP can recover -sparse trigonometric polynomials. Based on Theorem 2.2 and Theorem 2.6 respectively, the following results give upper bounds of with taking , and , respectively.
Lemma 3.1**.**
- (1)
Suppose that where and is a prime number. Then
[TABLE] 2. (2)
Suppose that are prime numbers and . Recall that
[TABLE]
Set and . Then
[TABLE]
As said before, if then OMP (and also BP) can recover every -sparse trigonometric polynomials. Then we have the following corollary:
Theorem 3.2**.**
- (1)
Suppose that is a prime number and . Then OMP (and also BP) recovers every -sparse trigonometric polynomial exactly from the deterministic sampling . 2. (2)
Under the condition in (2) of Lemma 3.1. Suppose that
[TABLE]
Then OMP (and also BP) recovers every -sparse trigonometric polynomial exactly from the deterministic sampling set .
Proof.
We first consider (1). Note that implies that . According to (1) in Lemma 3.1, if then and hence the conclusion follows. Similarly, we can prove (2). ∎
The reference list from the paper itself. Each links out to its DOI / PubMed record.
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- 5[5] N. M. Korobov, Number-theoretic methods in approximate analysis. Gosudarstv. Izdat. Fiz.-Mat. Lit., Moscow, 1963.
- 6[6] S. Kunis, H. Rauhut, Random sampling of sparse trigonometric polynomials II- Orthogonal matching pursuit versus basis pursuit, Foundations of Computational Mathematics, 8(6), 1615-3375, 2008.
- 7[7] Gunther Leobacher, Friedrich Pillichshammer, Introduction to quasi-Monte Carlo integration and applications, Compact Textbook in Mathematics, Birkh a ¨ ¨ 𝑎 \ddot{a} user/Springer, Cham, 2014.
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