Abstract
Positive definite homogeneous multivariate forms play an important role in polynomial problems and medical imaging, and the definiteness of forms can be tested using structured tensors. In this paper, we state the equivalence between the positive definite multivariate forms and the corresponding tensors, and explain the connection between the positive definite tensors with -tensors. Then, based on the notion of diagonally dominant tensors, some criteria for -tensors are presented. Meanwhile, with these links, we provide an iterative algorithm to test the positive definiteness of multivariate homogeneous forms and prove its validity theoretically. The advantages of the obtained results are illustrated by some numerical examples.
Keywords:
homogeneous multivariate form; positive definiteness; ℋ-tensor; diagonal dominance; irreducible; non-zero element chain; iterate scheme MSC:
15A18; 15A69; 65F15; 65H17
1. Introduction
Let be the complex (real) field and . is called an mth-order n-dimensional complex (real) tensor [1,2], if
where for . It is denoted by . Tensor is called symmetric [3], if
where is the permutation group of m indices. Tensor is called the unit tensor [4], if
Tensor is called a diagonally dominant tensor, if
is strictly diagonally dominant if all strict inequalities in hold. If there exists a number and a non-zero vector , such that
then is called an eigenvalue of and x is called the eigenvector of associated with [5,6,7], where and are vectors, whose ith components are
When and , we call an H-eigenvalue of and x an H-eigenvector of associated with [1].
An mth degree homogeneous polynomial of n variables can be defined as
where is symmetric and [8]. When m is even, we call that is positive definite if
The tensor is called positive definite if is positive definite [8].
The positive definiteness of multivariate polynomial plays an important role in automatical control and polynomial problems [9,10]. However, when and , it is difficult to test the positive definiteness for multivariate forms. To solve this problem, Qi [1] provided that as is positive definite, if and only if the real symmetric tensor is positive definite, and presented an eigenvalue way to identify the positive definiteness of with even m (see Proposition 1).
Proposition 1
([1]).Let be even-order symmetric. Then is positive definite if and only if its H-eigenvalues are all positive.
However, it is not easy to compute all H-eigenvalues of when m and n are large. Recently, Li et al. [11] gave a sufficient condition of the positive definiteness for an even-order symmetric tensor by -tensors (see Proposition 2). Here, a tensor is called an -tensor [12] if there is a positive vector satisfying
Proposition 2
([11]).Let be even-order symmetric with positive diagonal entries. If is an -tensor, then is positive definite.
Subsequently, based on the notion of diagonally dominant tensors, various criterions for -tensors are established [13,14,15,16,17,18,19,20,21], which only depend on the elements of the tensors, and are very effective for testing whether a tensor is an -tensor or not. For example,
Theorem 1
([17]).Let . If
where
then is an -tensor.
Theorem 2
([18]).Let . If
and
where
then is an -tensor.
Theorem 3
([21]).Let . If
and for , , where , , and are that as in Theorem 2, then is an -tensor.
We know that these criterions can be used just for a finite class of -tensors; that is, there are a lot of -tensors that cannot be identified by these existing criteria, and therefore, many authors consider whether there exists a more effective iterative scheme for testing -tensors [13,14,15,16,17,18,19,20]. However, some algorithms need to use a parameter , and it is hard to choose a suitable value for , and an inappropriate may result in a large computing quantity. In this paper, we continue to propose some new sufficient conditions of -tensors, and we present a new non-parameter-involved iterative algorithm for testing -tensors. The obtained results extend the corresponding results in [17,18,19,20,21]. The validity of new methods is theoretically guaranteed, and the numerical experiments show their efficiency.
The remainder of this paper is organized as follows. In Section 2, we recall some related definitions, lemmas, and notations, which will be used in the proof of this paper. In Section 3, we explore some new sufficient conditions for identifying -tensors, only relying on the elements of such tensors. In Section 4, we further propose a non-parameter implementable iterative algorithm for identifying -tensors; the efficiency of the scheme is theoretically guaranteed in this section. In Section 5, as an application, we provide new conditions for testing the positive definiteness of even-order homogeneous multivariate forms. Some conclusions are summarized in Section 6.
2. Preliminaries
Now, some definitions, lemmas, and notations are recalled, which will be used in the sequel.
Definition 1
([4]).Let . is called reducible, if there exists a non-empty proper index subset , such that
We call irreducible if is not reducible.
Definition 2
([2]).Let and . Denote
We call the product of the tensor and the matrix X.
Definition 3
([13]).Let . For , if there exist indices with
where , then it is called that there is a non-zero elements chain from i to j.
Lemma 1
([11]).If is strictly diagonally dominant, then is an -tensor.
Lemma 2
([2]).Let . If there exists a positive diagonal matrix X, such that is an -tensor, then is an -tensor.
Lemma 3
([11]).Let . If is irreducible,
and strict inequality holds for at least one i, then is an -tensor.
Lemma 4
([13]).Let . If
- (i)
- ,
- (ii)
- ,
- (iii)
- For any , there exists a non-zero elements chain form i to j, such that , then is an -tensor.
For convenience, some notations that will be used in the following are given below. Let S be a non-empty subset of N, and let be the complement of S in N. Given a tensor with , , and
and
and
3. Criteria for Identifying -Tensors
In this section, we give some new criteria for identifying -tensors.
Theorem 4.
Let . If there exists , such that
and there exists for any , such that , then is an -tensor.
Proof.
According to the definitions of , , by and for , we have
so, , and
From the definition of , we obtain:
so , and
For any , let
When , denote . By (3) and (5), we have . Since , then there is a sufficiently small positive number , such that
Let diagonal matrix , where
As , , which implies that X is a diagonal matrix with positive entries. Let . Next, we will prove that is a strictly diagonally dominant tensor.
(1) For any , since
then
(2) For any , if , we have
If , by Equality (5), we get
(3) For any , by Inequality (4), it holds that
Hence, by Inequalities (6)–(9), it holds that ; that is, is a strictly diagonally dominant tensor. From Lemma 1, is an -tensor. By Lemma 2, is an -tensor. □
Theorem 5.
Let be irreducible. If there exists such that
and at least one strict inequality in holds, then is an -tensor.
Proof.
Let diagonal matrix , where
This shows that X is a diagonal matrix with positive entries. Let . Next, we prove that is a diagonally dominant tensor.
(1) For , since
then
(2) For , if , we obtain
If , by Inequality (10), we get
(3) For , by Inequality (4), it holds that
Therefore, by Inequalities (11)–(14), we conclude that for all , and for all , at least one strict inequality in (12) and (13) holds; that is, there exists an satisfying .
Meanwhile, is irreducible, and so is . By Lemma 3, we obtain that is an -tensor. From Lemma 2, is also an -tensor. □
Let
Theorem 6.
Let . For any , there exists such that
and for , there is a non-zero elements chain from i to j satisfying , then is an -tensor.
Proof.
Denote diagonal matrix as that in the proof of Theorem 5. This shows that X is a diagonal matrix with positive entries. Mark . Similar to the process in proof of Theorem 4, it holds that , and there is at least one .
On the other hand, if , then . We know that there exists a chain of non-zero elements from i to j in , with . So, there is also a non-zero element chain from i to j in satisfying . Hence, satisfies the conditions of Lemma 4; that is, is an -tensor. From Lemma 2, is also an -tensor. □
Example 1.
Given tensor , defined as follows:
By calculations, we have
Then , , , and
When and , since
then satisfies the conditions of Theorem 4; that is, is an -tensor. However,
and
Therefore, does not satisfy the conditions of Theorem 1 (Theorem 1 in [17]), Theorem 2 (Theorem 1 in [18]), and Theorem 3 (Theorem 2 in [21]), respectively.
Remark 1.
By Example 1, it is easy to see that Theorem 4 is better than the other ones, but it is difficult to say in advance which one is better. However, from Theorem 4, we conclude that , , and
Thus, all conditions in Theorem 4 are weaker than those in Theorem 1.
4. An Iterative Algorithm for Testing -Tensors
In this section, we propose a new iterative scheme for testing -tensors based on the obtained results in Section 3.
The theoretical analysis of Algorithm 1 can be obtained from the following result.
| Algorithm 1: A new iterative scheme for identifying -tensors. |
| INPUT: with , . |
| OUTPUT: diagonal matrix with positive entries if is an -tensor. |
|
|
|
|
|
|
|
|
|
Theorem 7.
Let be an -tensor. Then, Algorithm 1 terminates after a finite number of iterations by producing a strictly diagonally dominant tensor.
Proof.
Without loss of generality, suppose that is a non-negative tensor. Instead, the Algorithm 1 yields an infinite sequence , with
By the fact that each diagonal entry of matrix is less than 1, we have
Hence, the sequence has a limitation,
where , is a diagonal matrix with positive entries.
Next, we will prove that
In fact, suppose that , then
Therefore, there exists and positive numbers , such that
Denote . Based on Algorithm 1, for , it holds that
Therefore,
When , one has . A contradiction arrives, which means that
that is,
Hence, is not an -tensor [11]. Meanwhile, if is an -tensor, then there is a diagonal matrix D with positive entries satisfying is a strictly diagonally dominant tensor. Hence, is an -tensor, which is a contradiction. Therefore, Algorithm 1 stops after a finite steps of iterations. □
Example 2
([17]).Randomly generate 100 mth-order n-dimensional tensors, such that the elements of each tensor satisfy
The numerical results of Algorithm 1 are shown in Table 1, where , , and denote the number of tensors that the input tensor is, or is not an -tensor, and undetermined, within a certain number of iterations, respectively.
Table 1.
Numerical results of Example 2.
Example 3
([14]).Randomly generate fourth-order 10-dimensional tensors satisfying that the elements of each tensor are from . For each tensor, the following modifications are satisfied: Each tensor can be symmetrized, and all diagonal entries can be replaced with absolute value; all diagonal entries of each tensor can be amplified to a degree such that some of these tensors are positive definite.
The numerical results of Algorithm 1 are shown in Table 2, where "Y", "N", and symbol "-" denote the output result that the input tensor is or is not positive definite and undetermined within a certain number of iterations, respectively.
Table 2.
Numerical results of Example 3.
5. An Application: The Positive Definiteness of Homogeneous Polynomial Forms
In this section, based on the above results for -tensors, new sufficient conditions for testing the positive definiteness of even-order real symmetric tensors are provided.
In view of Proposition 2, the following result is easily obtained.
Theorem 8.
Let be an even-order symmetric tensor with . Tensor is positive definite if satisfies one of the following conditions:
- the conditions of Theorem 4;
- the conditions of Theorem 5;
- the conditions of Theorem 6.
Example 4.
Consider the following sixth-degree homogeneous polynomial
where and is a real symmetric tensor with elements defined as follows:
and other . Since
then , , . By calculations, we have
and
So, satisfies the conditions of Theorem 4. By Theorem 8, we have that is positive definite; that is, is positive definite.
However, for , we have
and
Hence, we cannot use Theorem 3 in in [19], Theorem 4 in in [20], Theorem 1 in [17], and Theorem 1 in [18] to identify the positive definiteness of , respectively.
6. Conclusions
In this paper, we presented new criterions for -tensors, which are used for testing the positive definiteness of an homogeneous polynomial. We also proposed a new non-parameter iterative algorithm for -tensors, which can stop within finite steps. These methods were expressed in terms of the elements of , so they can be checked easily.
Author Contributions
Conceptualization, D.B.; Data curation, D.B.; Formal analysis, D.B.; Funding acquisition, F.W.; Investigation, F.W.; Methodology, F.W.; Resources, F.W.; Software, D.B.; Supervision, F.W.; Validation, F.W.; Writing-original draft, D.B. and F.W.; Writing-review & editing, F.W. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by Guizhou Provincial Science and Technology Projects (20191161, 20181079), the Talent Growth Project Department of Guizhou Province (2016168), the Foundation of Education Department of Guizhou Province (2018143), and the Research Foundation of Guizhou Minzu University (2019YB08).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Acknowledgments
The authors are grateful to editors and referees for their valuable suggestions and comments.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Qi, L.Q. Eigenvalues of a real supersymetric tensor. J. Symb. Comput. 2005, 40, 1302–1324. [Google Scholar] [CrossRef]
- Kannana, M.R.; Mondererb, N.S.; Bermana, A. Some properties of strong -tensors and general -tensors. Linear Algebra Appl. 2015, 476, 42–55. [Google Scholar] [CrossRef]
- Li, C.Q.; Qi, L.Q.; Li, Y.T. MB-tensors and MB0-tensors. Linear Algebra Appl. 2015, 484, 141–153. [Google Scholar] [CrossRef]
- Yang, Y.N.; Yang, Q.Z. Further results for Perron-Frobenius Theorem for nonnegative tensors. SIAM J. Matrix Anal. Appl. 2010, 31, 2517–2530. [Google Scholar] [CrossRef]
- Kolda, T.G.; Mayo, J.R. Shifted power method for computing tensor eigenpairs. SIAM J. Matrix Anal. Appl. 2011, 32, 1095–1124. [Google Scholar] [CrossRef]
- Lim, L.H. Singular values and eigenvalues of tensors: A variational approach. In Proceedings of the 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Puerto Vallarta, Mexico, 13–15 December 2005; pp. 129–132. [Google Scholar]
- Qi, L.Q.; Wang, F.; Wang, Y.J. Z-eigenvalue methods for a global polynomial optimization problem. Math. Program. 2009, 118, 301–316. [Google Scholar] [CrossRef]
- Ni, Q.; Qi, L.; Wang, F. An eigenvalue method for testing positive definiteness of a multivariate form. IEEE Trans. Automat. Control. 2008, 53, 1096–1107. [Google Scholar] [CrossRef]
- Ni, G.Y.; Qi, L.Q.; Wang, F.; Wang, Y.J. The degree of the E-characteristic polynomial of an even order tensor. J. Math. Anal. Appl. 2007, 329, 1218–1229. [Google Scholar] [CrossRef][Green Version]
- Zhang, L.P.; Qi, L.Q.; Zhou, G.L. -tensors and some applications. SIAM J. Matrix Anal. Appl. 2014, 35, 437–542. [Google Scholar] [CrossRef]
- Li, C.Q.; Wang, F.; Zhao, J.X. Criterions for the positive definiteness of real supersymmetric tensors. J. Comput. Appl. Math. 2014, 255, 1–14. [Google Scholar] [CrossRef]
- Ding, W.Y.; Qi, L.Q.; Wei, Y.M. -tensors and nonsingular -tensors. Linear Algebra Appl. 2013, 439, 3264–3278. [Google Scholar] [CrossRef]
- Wang, F.; Sun, D.S.; Zhao, J.X.; Li, C.Q. New practical criteria for -tensors and its application. Linear Multilinear Algebra 2017, 65, 269–283. [Google Scholar] [CrossRef]
- Zhang, K.L.; Wang, Y.J. An -tensor based iterative scheme for identifying the positive definiteness of multivariate homogeneous forms. J. Comput. Appl. Math. 2016, 305, 1–10. [Google Scholar] [CrossRef]
- Wang, Y.J.; Zhou, G.L.; Caccetta, L. Nonsingular -tensor and its criteria. J. Ind. Manag. Optim. 2016, 12, 1173–1186. [Google Scholar] [CrossRef]
- Wang, Y.J.; Zhang, K.L.; Sun, H.C. Criteria for strong -tensors. Front. Math. China. 2016, 11, 577–592. [Google Scholar] [CrossRef]
- Li, Y.T.; Liu, Q.L.; Qi, L.Q. Programmable criteria for strong -tensors. Numer. Algor. 2017, 74, 199–221. [Google Scholar] [CrossRef]
- Wang, F.; Sun, D.S.; Xu, Y.M. Some criteria for identifying -tensors and its applications. Calcolo 2019, 56, 19. [Google Scholar] [CrossRef]
- Qi, L.Q.; Song, Y.S. An even order symmetric -tensor is positive definite. Linear Algebra Appl. 2014, 457, 303–312. [Google Scholar] [CrossRef]
- Li, C.Q.; Li, Y.T. Double -tensors and quasi-double -tensors. Linear Algebra Appl. 2015, 466, 343–356. [Google Scholar] [CrossRef]
- Bai, D.J.; Wang, F. New methods based -tensors for identifying the positive definiteness of multivariate homogeneous forms. AIMS Math. 2021, 6, 10281–10295. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).