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Open Mathematics

formerly Central European Journal of Mathematics

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Volume 15, Issue 1


Volume 13 (2015)

New bounds for the minimum eigenvalue of 𝓜-tensors

Jianxing Zhao
  • Corresponding author
  • College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, China
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/ Caili Sang
Published Online: 2017-03-16 | DOI: https://doi.org/10.1515/math-2017-0018


A new lower bound and a new upper bound for the minimum eigenvalue of an 𝓜-tensor are obtained. It is proved that the new lower and upper bounds improve the corresponding bounds provided by He and Huang (J. Inequal. Appl., 2014, 2014, 114) and Zhao and Sang (J. Inequal. Appl., 2016, 2016, 268). Finally, two numerical examples are given to verify the theoretical results.

Keywords: 𝓜-tensors; Nonnegative tensors; Minimum eigenvalue

MSC 2010: 15A18; 15A42; 15A69

1 Introduction

Let ℂ(ℝ) be the set of all complex (real) numbers, n be positive integer, n ≥ 2, and N = {1, 2, ⋯, n}. We call 𝒜 = (ai1i2 im) a complex (real) tensor of order m dimension n, if ai1i2imC(R), where ijN for j = 1, ⋯, m. Obviously, a vector is a tensor of order 1 and a matrix is a tensor of order 2. We call 𝒜 nonnegative if 𝒜 is real and each of its entries ai1 im ≥ 0. Let ℝ[m, n] denote the set of real tensors with order m dimension n.

A tensor 𝒜 = (ai1i2im) of order m dimension n is called reducible if there exists a nonempty proper index subset αN such that ai1i2im=0,i1α,i2,,imα.

If 𝒜 is not reducible, then we call 𝒜 irreducible [1].

For a complex tensor 𝒜 = (ai1i2 im) of order m dimension n, if there are a complex number λ and a nonzero complex vector x = (x1, x2, ⋯, xn)T that are solutions of the following homogeneous polynomial equations Axm1=λx[m1], then λ is called an eigenvalue of 𝒜 and x an eigenvector of 𝒜 associated with λ, where 𝒜xm − 1 and x[m − 1] are vectors, whose i th component are (Axm1)i=i2,,imNaii2imXi2Xim, and (x[m1])i=xim1, respectively. This definition was introduced by Qi in [2], where he assumed that 𝒜 is an order m dimension n supersymmetric tensor and m is even. Independently, in [3], Lim gave such a definition but restricted x to be a real vector and λ to be a real number. In this case, we call λ an H-eigenvalue of 𝒜 and x an H-eigenvector of 𝒜 associated with λ.

Moreover, the spectral radius ρ(𝒜) of the tensor 𝒜 is defined as ρ(A)=max{|λ|:λσ(A)}, where σ(𝒜) is the spectrum of 𝒜, that is, σ(𝒜) = {λ : λ is an eigenvalue of 𝒜}; see [1, 4].

The class of 𝓜-tensors introduced in [5, 6] is related to nonnegative tensors, which is an generalization of M-matrices [7].

Definition 1.1

([5, 6)]. Let 𝒜 = (ai1i2 im) ∈ ℝ[m, n]. 𝒜 is called

  1. a 𝒵-tensor if all of its off-diagonal entries are non-positive;

  2. an 𝓜-tensor if 𝒜 is a 𝒵-tensor with the from 𝒜 = cℐ − ℬ such thatis a nonnegative tensor and c > ρ(ℬ), where ρ(ℬ) is the spectral radius of ℬ, andis called the unit tensor with its entries δi1im=1,i1==im,0,otherwise.

Theorem 1.2

[5, 8] Let 𝒜 be an 𝓜-tensor and denote by τ(𝒜) the minimal value of the real part of all eigenvalues of 𝒜. Then τ(𝒜)>0 is an eigenvalue of 𝒜 with a nonnegative eigenvector. If 𝒜 is irreducible, then τ(𝒜) is the unique eigenvalue with a positive eigenvector.

Eigenvalue problems of tensors have become an important topic of study in numerical multilinear algebra, and received much attention in the literature; see [519]. In [8], He and Huang provided some lower and upper bounds on τ(𝒜) for an irreducible 𝓜-tensor 𝒜.

Theorem 1.3

([8, Theorem 2.1]). Let 𝒜 = (ai1i2 im) ∈ ℝ[m, n] be an irreducible 𝓜-tensor. Then τ(A)miniNaiii,andminiNRi(A)τ(A)maxiNRi(A), where Ri(A)=i2,,imNaii2im.

In order to obtain more sharper bounds of the minimum eigenvalue for an irreducible 𝓜-tensor, Zhao and Sang [9] gave a lower bound which estimates the minimum eigenvalue more precisely than that in Theorem 1.3.

Theorem 1.4

([9, Theorem 4]). Let 𝒜 = (ai1i2 im) ∈ ℝ[m, n] be an irreducible 𝓜-tensor. Then τ(A)mini,jNjjLij(A),where Lij(A)=12{aii+ajjrij(A)[aiiajjrij(A))24aijjrj(A)]12},ri(A)=i2,,imN,δii2im=0|aii2im|,rij(A)=δii2im=0,δji2im=0|aii2im|=ri(A)|aijj|.

In this paper, we continue this research, and give a lower bound and an upper bound for τ(𝒜) of an 𝓜-tensor. It is proved that these bounds are better than the corresponding bounds in [8] and [9]. Finally, two numerical examples are given to verify the obtained results.

2 Main results

In this section, we give a new lower bound and a new upper bound for the minimum eigenvalue of an 𝓜-tensors and establish the comparison between the new bounds with those in Theorem 1.3 and Theorem 1.4. For simplification, we denote Δi={(i2,i3,im):ij=iforsomej{2,,m},wherei,i2,,jmN},Δ¯i={(i2,i3,im):ijiforanyj{2,,m},wherei,i2,,jmN}.

Given a tensor 𝒜 = (ai1i2 im) ∈ ℝ[m, n], let riΔi(A)=(i2,,im)Δi,δii2im=0|aii2im|,riΔ¯i(A)=(i2,,im)Δ¯i|aii2im|.

Obviously, ri(A)=riΔi(A)+riΔ¯i(A),rij(A)=riΔi(A)+riΔ¯i(A)|aijj|.

Theorem 2.1

Let 𝒜 = (ai1 im) ∈ ℝ[m, n] be an irreducible 𝓜-tensor. Then mini,jN,jiΩij(A)τ(A)maxi,jN,jiΩij(A),(1) where Ωij(A)=12{aii+ajjriΔi(A)[(aiiajjriΔi(A))2+4riΔ¯i(A)rj(A)]12}.


  1. Because τ(𝒜) is an eigenvalue of 𝒜, from Theorem 2.1 in [10], there are i, jN, ji, such that (|τ(A)aii|riΔi(A))|τ(A)ajj|riΔ¯i(A)rj(A).

    From Theorem 1.3, we can get (aiiτ(A)riΔi(A))(ajjτ(A))riΔ¯i(A)rj(A), equivalently, τ(A)2(aii+ajjriΔi(A))τ(A)+ajj(aiiriΔi(A))riΔ¯i(A)rj(A)0.

    Solving for τ(𝒜) gives τ(A)12{aii+ajjriΔi(A)[(aii+ajjriΔi(A))24(ajj(aiiriΔi(A))riΔ¯i(A)rj(A))]12}=12{aii+ajjriΔi(A)[(aiiajjriΔi(A))2+4riΔ¯i(A)rj(A)]12}mini,jNji12{aii+ajjriΔi(A)[(aiiajjriΔi(A))2+4riΔ¯i(A)rj(A)]12}.

  2. Next, we prove that the second inequality in (1) holds. Let x = (x1, x2, ⋯, xn)T be an associated positive eigenvector of 𝒜 corresponding to τ(𝒜), i.e., Axm1=τ(A)x[m1].(2)

    Without loss of generality, suppose that xtnxtn1xt2xt1.

    From (2), we have i2,,imNat1i2imxi2xim=τ(A)xt1m1, equivalently, (at1t1τ(A))xt1m1=(i2,,im)Δt1δt1i2,,im=0,|at1i2im|xi2xim+(i2,,im)Δ¯t1|at1i2im|xi2xim.

    Hence, (at1t1τ(A))xt1m1(i2,,im)Δt1,δt1i2,,im=0|at1i2im|xt1m1+(i2,,im)Δ¯t1|at1i2im|xt2m1=rt1Δt1(A)xt1m1+rt1Δ¯t1(A)xt2m1, i. e., (at1t1rt1Δt1(A)τ(A))xt1m1rt1Δ¯t1(A)xt2m10.(3)

    Similarly, we have, from (2), i2,,imNat2i2imxi2xim=τ(A)xt2m1.

    Furthermore, (at2t2τ(A))xt2m1=i2,,imNδt2i2im=0|at2i2im|xi2ximi2,,imNδt2i2im=0|at2i2im|xt1m1=rt2(A)xt1m10.(4)

    Multiplying inequality (3) and inequality (4) gives (at1t1rt1Δt1(A)τ(A))(at2t2τ(A))xt1m1xt2m1rt1Δ¯t1(A)rt2(A)xt1m1xt2m1.

    Note that xt2xt1 > 0, hence (at1t1tt1Δt1(A)τ(A))(at2t2τ(A))rt1Δ¯t1(A)rt2(A), that is, τ(A)2(at1t1+at2t2rt1Δt1(A))τ(A)+at2t2(at1t1rt1Δt1(A))rt1Δ¯t1(A)rt2(A)0.(5)

    From (3), we have τ(A)at1t1rt1Δt1(A). From (4), we have τ(𝒜) ≤ at2t2. Then τ(A)12{at1t1+at2t2rt1Δt1(A)}.

    Thus, solving for τ(𝒜) from (5) gives τ(A)12{at1t1+at2t2rt1Δt1(A)[(at1t1+at2t2rt1Δt1(A))24(at2t2(at1t1rt1Δt1(A))rt1Δ¯t1(A)rt2(A))]12}=12{at1t1+at2t2rt1Δt1(A)[(at1t1at2t2rt1Δt1(A))2+4rt1Δ¯t1(A)rt2(A))]12}maxi,jNji12{aii+ajjriΔi(A)[(aiiajjriΔi(A)2+4riΔ¯i(A)rj(A)]12}.

    The conclusion follows from I and II. □

    Similarly to the proof of Theorem 3.6 in [11], we can extend the results of Theorem 2.1 to general 𝓜-tensors.

Theorem 2.2

Let 𝒜 = (ai1im) ∈ ℝ[m, n] be an 𝓜-tensor. Then mini,jNjiΩij(A)τ(A)maxi,jNjiΩij(A).

Next, we compare the bounds in Theorem 2.1 with those in Theorem 1.3 and Theorem 1.4.

Theorem 2.3

Let 𝒜 = (ai1 im) ∈ ℝ[m, n] be an irreducible 𝓜-tensor. Then miniNRi(A)mini,jNjiLij(A)mini,jNjiΩij(A)maxi,jNjiΩij(A)maxiNRi(A).(6)


  1. From Theorem 5 in [9], we have miniNRi(A)mini,jNjiLij(A). Obviously, the first inequality in (6) holds.

  2. From Theorem 2.4 in [10], the proof of Theorem 4 in [9] and Theorem 2.1, it is easy to see that mini,jNjiLij(A)mini,jNjiΩij(A), that is, the second inequality in (6) holds.

  3. Next, we prove that the last inequality in (6) holds.

    1. For any i, jN, ji, if Rj(𝒜) ≤ Rj(𝒜), i.e., aiiriΔi(A)riΔ¯i(A)ajjr(A), then riΔ¯i(A)aiiajjriΔi(A)+rj(A).

      Hence, [aiiajjriΔi(A)]2+4riΔ¯i(A)rj(A)[aiiajjriΔi(A)]2+4[aiiajjriΔi(A)+rj(A)]rj(A)=[aiiajjriΔi(A)]2+4[aiiajjriΔi(A)]rj(A)+4[rj(A)]2=[aiiajjriΔi(A)+2rj(A)]2.

      When aiiajjriΔi(A)+2rj(A)>0, we have aii+ajjriΔi(A)[(aiiajjriΔi(A))2+4riΔ¯i(A)rj(A)]12aii+ajjriΔi(A)[aiiajjriΔi(A)+2rj(A)]=2ajj2rj(A)=2Rj(A).

      When aiiajjriΔi(A)+2rj(A)0, that is, aiiriΔi(A)ajjj2rj(A), we have aii+ajjriΔi(A)[(aiiajjriΔi(A))2+4riΔ¯i(A)rj(A)]12aii+ajjriΔi(A)[(aiiajjriΔi(A))2]12=aii+ajjriΔi(A)|aiiajjriΔi(A)|=aii+ajjriΔi(A)+[aiiajjriΔi(A)]=2aii2riΔi(A)2ajj4rj(A)2ajj2rj(A)=2Rj(A).

      Therefore, Ωij(A)=12{aii+ajjriΔi(A)[(aiiajjriΔi(A))2+4riΔi¯(A)rj(A)]12}Rj(A), which implies maxi,jNjiΩij(A)maxjNRj(A).

    2. For any i, jN, ji, if Rj(𝒜) ≤ Rj(𝒜), i.e., ajjrj(A)aiiriΔi(A)riΔ¯i(A), then rj(A)ajjaii+riΔi(A)+riΔ¯i(A).

      Similarly, we can obtain Ωij(A)=12{aii+ajjriΔi(A)[(aiiajjriΔi(A))2+4riΔi¯(A)rj(A)]12}Ri(A), which implies maxi,jNjiΩij(A)maxiNRi(A).

      The conclusion follows from I, II and III. □

Remark 2.4

Theorem 2.3 shows that the bounds in Theorem 2.1 are better than those in Theorem 1.3 and Theorem 1.4.

3 Numerical examples

In this section, two numerical examples are given to verify the theoretical results.

Example 3.1

Let 𝒜 = (aijk) ∈ ℝ[3,4] be an irreducible 𝓜-tensor with elements defined as follows: A(1,:,:)=63213334323123432,A(2,:,:)=23443464131421333,A(3,:,:)=33332232325322212,A(4,:,:)=23212233422333254.

By Theorem 1.3, we have 5τ(A)24.

By Theorem 1.4, we have τ(A)5.6402.

Let S={1,2},S¯={3,4}. By Theorem 6 in [9], we have τ(A)6.0200.

By Theorem 2.1, we have 9.4206τ(A)21.3521.

In fact, τ(𝒜) = 15.3013. Hence, this example shows that the bounds in Theorem 2.1 are better than those in Theorem 1.3, Theorem 1.4 and Theorem 6 in [9].

Example 3.2

Let 𝒜 = (aijkl) ∈ ℝ[4,2] be an irreducible 𝓜-tensor with elements defined as follows: a1111=5,a1222=1,a2111=2,a2222=4, other aijkl = 0. By Theorem 1.3, we have 2τ(A)4.

By Theorem 1.4, we have τ(A)3.

Let S={1,2},S¯={3,4}.

By Theorem 6 in [9], we have τ(A)3.

By Theorem 2.1, we have 3τ(A)3.

In fact, τ(𝒜) = 3. Hence, the bounds in Theorem 2.1 are tight and sharper than those in Theorem 1.3.

4 Conclusions

In this paper, we obtain a lower bound and an upper bound for the minimum eigenvalue of an 𝓜-tensors, which improved the known bounds obtained by He and Huang [8], and Zhao and Sang [9].


The authors are very indebted to the reviewers for their valuable comments and corrections, which improved the original manuscript of this paper. This work is supported by the National Natural Science Foundation of China (Nos. 11361074, 11501141), the Natural Science Programs of Education Department of Guizhou Province (Grant No.[2016]066), and the Foundation of Guizhou Science and Technology Department (Grant No.[2015]2073).


  • [1]

    Chang K.Q., Zhang T., Pearson K., Perron-Frobenius theorem for nonnegative tensors, Commun. Math. Sci., 2008, 6, 507-520 CrossrefGoogle Scholar

  • [2]

    Qi L.Q., Eigenvalues of a real supersymmetric tensor, J. Symb. Comput., 2005, 40, 1302-1324 CrossrefGoogle Scholar

  • [3]

    Lim L.H., Singular values and eigenvalues of tensors: A variational approach, Proceedings of the IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP’05), 2015, 1, 129-132 Google Scholar

  • [4]

    Yang Y.N., Yang Q.Z., Further results for Perron-Frobenius theorem for nonnegative tensors, SIAM J. Matrix Anal. Appl., 2010, 31, 2517-2530 CrossrefGoogle Scholar

  • [5]

    Zhang L.P., Qi L.Q., Zhou G.L., 𝓜-tensors and some applications, SIAM J. Matrix Anal. Appl., 2014, 35, 437-452 Web of ScienceCrossrefGoogle Scholar

  • [6]

    Ding W.Y., Qi L.Q., Wei Y.M., 𝓜-tensors and nonsingular 𝓜-tensors, Linear Algebra Appl., 2013, 439, 3264-3278 CrossrefGoogle Scholar

  • [7]

    Berman A., Plemmons R.J., Nonnegative matrices in the mathematical sciences, SIAM, Philadelphia, 1994 Google Scholar

  • [8]

    He J., Huang T.Z., Inequalities for 𝓜-tensors, J. Inequal. Appl., 2014, 2014, 114 CrossrefGoogle Scholar

  • [9]

    Zhao J.X., Sang C.L., Two new lower bounds for the minimum eigenvalue of 𝓜-tensors, J. Inequal. Appl., 2016, 2016, 268 Web of ScienceCrossrefGoogle Scholar

  • [10]

    Li C.Q., Li Y.T., An eigenvalue localization set for tensors with applications to determine the positive (semi-)definiteness of tensors, Linear Multilinear Algebra, 2016, 64(4), 587-601 Web of ScienceCrossrefGoogle Scholar

  • [11]

    Huang Z.G., Wang L.G., Xu Z., Cui J.J., A new S-type eigenvalue inclusion set for tensors and its applications, J. Inequal. Appl., 2016, 2016, 254 Web of ScienceCrossrefGoogle Scholar

  • [12]

    Zhou D.M., Chen G.L., Wu G.X., Zhang X.Y., On some new bounds for eigenvalues of the Hadamard product and the Fan product of matrices, Linear Algebra Appl., 2013, 438, 1415-1426Web of ScienceCrossrefGoogle Scholar

  • [13]

    Li C.Q., Qi L.Q., Li Y.T., 𝓜ℬ-tensors and 𝓜ℬ0-tensors, Linear Algebra Appl., 2015, 484, 141–153CrossrefGoogle Scholar

  • [14]

    Li C.Q., Zhang C.Y., Li Y.T., Minimal Geršgorin tensor eigenvalue inclusion set and its approximation, J. Comput. Appl. Math., 2016, 302, 200-210CrossrefGoogle Scholar

  • [15]

    Zhou J., Sun L.Z., Wei Y.P., Bu C.J., Some characterizations of 𝓜-tensors via digraphs, Linear Algebra Appl., 2016, 495, 190–198CrossrefWeb of ScienceGoogle Scholar

  • [16]

    Li C.Q, Li Y.T., Kong X., New eigenvalue inclusion sets for tensors, Numer. Linear Algebra Appl., 2014, 21, 39-50CrossrefWeb of ScienceGoogle Scholar

  • [17]

    He J., Liu Y.M., Ke H., Tian J.K., Li X., Bounds for the Z-spectral radius of nonnegative tensors, Springerplus, 2016, 5(1), 1727Web of ScienceCrossrefGoogle Scholar

  • [18]

    He J., Liu Y.M., Ke H., Tian J.K., Li X., Bound for the largest singular value of nonnegative rectangular tensors, Open Math., 2016, 14(1), 761-766Web of ScienceGoogle Scholar

  • [19]

    He J., Bounds for the largest eigenvalue of nonnegative tensors, J. Comput. Anal. Appl., 2016, 20(7), 1290-1301 Google Scholar

About the article

Received: 2016-11-06

Accepted: 2017-01-16

Published Online: 2017-03-16

Citation Information: Open Mathematics, Volume 15, Issue 1, Pages 296–303, ISSN (Online) 2391-5455, DOI: https://doi.org/10.1515/math-2017-0018.

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© 2017 Zhao and Sang. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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