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## Advances in Mathematical Methods for Image and Signal Processing

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Research Article | Open Access

Volume 2014 |Article ID 239693 | https://doi.org/10.1155/2014/239693

Jin-jiang Yao, Zhao-lin Jiang, "The Determinants, Inverses, Norm, and Spread of Skew Circulant Type Matrices Involving Any Continuous Lucas Numbers", Journal of Applied Mathematics, vol. 2014, Article ID 239693, 10 pages, 2014. https://doi.org/10.1155/2014/239693

# The Determinants, Inverses, Norm, and Spread of Skew Circulant Type Matrices Involving Any Continuous Lucas Numbers

Accepted17 Feb 2014
Published17 Apr 2014

#### Abstract

We consider the skew circulant and skew left circulant matrices with any continuous Lucas numbers. Firstly, we discuss the invertibility of the skew circulant matrices and present the determinant and the inverse matrices by constructing the transformation matrices. Furthermore, the invertibility of the skew left circulant matrices is also discussed. We obtain the determinants and the inverse matrices of the skew left circulant matrices by utilizing the relationship between skew left circulant matrices and skew circulant matrix, respectively. Finally, the four kinds of norms and bounds for the spread of these matrices are given, respectively.

#### 1. Introduction

Circulant and skew-circulant matrices are appearing increasingly often in scientific and engineering applications. Briefly, scanning the recent literature, one can see their utility is appreciated in the design of digital filters [1â€“3], image processing [4â€“6], communications [7], signal processing [8], and encoding [9]. They have been put on firm basis with the work of Davis [10] and Jiang and Zhou [11].

The skew circulant matrices as preconditioners for linear multistep formulae- (LMF-) based ordinary differential equations (ODEs) codes. Hermitian and skew-Hermitian Toeplitz systems are considered in [12â€“15]. Lyness and SĂ¸revik employed a skew circulant matrix to construct -dimensional lattice rules in [16]. Spectral decompositions of skew circulant and skew left circulant matrices were discussed in [17]. Compared with cyclic convolution algorithm, the skew cyclic convolution algorithm [8] is able to perform filtering procedure in approximate half of computational cost for real signals. In [2] two new normal-form realizations are presented which utilize circulant and skew circulant matrices as their state transition matrices. The well-known second-order coupled form is a special case of the skew circulant form. Li et al. [18] gave the style spectral decomposition of skew circulant matrix firstly and then dealt with the optimal backward perturbation analysis for the linear system with skew circulant coefficient matrix. In [3], a new fast algorithm for optimal design of block digital filters (BDFs) was proposed based on skew circulant matrix.

Besides, some scholars have given various algorithms for the determinants and inverses of nonsingular circulant matrices [10, 11]. Unfortunately, the computational complexity of these algorithms is very amazing with the order of matrix increasing. However, some authors gave the explicit determinants and inverse of circulant and skew circulant involving some famous numbers. For example, Jaiswal evaluated some determinants of circulant whose elements are the generalized Fibonacci numbers [19]. Lind presented the determinants of circulant and skew circulant involving the Fibonacci numbers [20]. Dazheng [21] gave the determinant of the Fibonacci-Lucas quasicyclic matrices. Shen et al. considered circulant matrices with the Fibonacci and Lucas numbers and presented their explicit determinants and inverses by constructing the transformation matrices [22]. Gao et al. [23] gave explicit determinants and inverses of skew circulant and skew left circulant matrices with the Fibonacci and Lucas numbers. Jiang et al. [24, 25] considered the skew circulant and skew left circulant matrices with the -Fibonacci numbers and the -Lucas numbers and discussed the invertibility of the these matrices and presented their determinant and the inverse matrix by constructing the transformation matrices, respectively.

Recently, there are several papers on the norms of some special matrices. Solak [26] established the lower and upper bounds for the spectral norms of circulant matrices with the classical Fibonacci and Lucas numbers entries. Ä°pek [27] investigated an improved estimation for spectral norms of these matrices. Shen and Cen [28] gave upper and lower bounds for the spectral norms of -circulant matrices in the forms of , , and they also obtained some bounds for the spectral norms of Kronecker and Hadamard products of matrix and matrix . Akbulak and Bozkurt [29] found upper and lower bounds for the spectral norms of Toeplitz matrices such that and . The convergence in probability and in distribution of the spectral norm of scaled Toeplitz, circulant, reverse circulant, symmetric circulant, and a class of -circulant matrices is discussed in [30].

Beginning with Mirsky [31], several authors [32â€“38] have obtained bounds for the spread of a matrix.

The purpose of this paper is to obtain the explicit determinants, explicit inverses, norm, and spread of skew circulant type matrices involving any continuous Lucas numbers. And we generalize the result [23]. In passing, the norm and spread of skew circulant type matrices have not been researched. It is hoped that this paper will help in changing this. More work continuing the present paper is forthcoming.

In the following, let be a nonnegative integer. We adopt the following two conventions , and, for any sequence , in the case .

The Lucas sequences are defined by the following recurrence relations [21â€“23, 27â€“29]: for . The first few values of the sequences are given by the following table:

The is given by the formula where and are the roots of the characteristic equation .

Definition 1 (see [17]). A skew circulant matrix over with the first row is meant a square matrix of the form denoted by .

Definition 2 (see [17]). A skew left circulant matrix over with the first row is meant a square matrix of the form denoted by .

Lemma 3 (see [10, 17]). Let be skew circulant matrix; then(i) is invertible if and only if the eigenvalues of â€‰where , , and ;(ii)if is invertible, then the inverse of is a skew circulant matrix.

Lemma 4 (see [17]). Let be skew left circulant matrix and let n be odd; then where , are the eigenvalues of .

Lemma 5 (see [23]). With the orthogonal skew left circulant matrix it holds that

Lemma 6 (see [23]). If then

Lemma 7 (see [27, 28]). Let be the Lucas numbers; then

Definition 8 (see [29]). Let be an matrix. The maximum column sum matrix norm, the spectral norm, the Euclidean (or Frobenius) norm, and the maximum row sum matrix norm of matrix are, respectively, where denotes the conjugate transpose of .

Lemma 9 (see [30]). If is an real symmetric or normal matrix, then one has where () are the eigenvalues of .

Definition 10 (see [31, 32]). Let be an matrix with eigenvalues , . The spread of is defined as

Beginning with Mirsky [31], several authors [32â€“38] have obtained bounds for the spread of a matrix.

Lemma 11. Let be an matrix. An upper bound for the spread due to Mirsky [31] states that where denotes the Frobenius norm of and is trace of .

Lemma 12 (see [38]). Let be an matrix; then(i)if is real and normal, then (ii)and if is Hermitian, then

#### 2. Determinant and Inverse of Skew Circulant Matrix with the Lucas Numbers

In this section, let be skew circulant matrix. Firstly, we give a determinant explicit formula for the matrix . Afterwards, we prove that is an invertible matrix for , and then we find the inverse of the matrix .

In the following, let , , , , and .

Theorem 13. Let be skew circulant matrix; then where is the th Lucas number. Specially, when , one gets the result of [23].

Proof. Obviously, satisfies the equation. In the case , let be two matrices; then we have where
So it holds that While taking , we have This completes the proof.

Theorem 14. Let be skew circulant matrix; then is an invertible matrix. Specially, when , one gets the result of [23].

Proof. Taking in, Theorem 13, we have . Hence is invertible. In the case , since , where , , we have where , . If there exists such that , we obtain , for , and hence it follows that is a real number. Since it yields that , so we have for . Since is not the root of the equation . We obtain , for any , while It follows from Lemma 3 that the conclusion holds.

Lemma 15. Let the matrix be of the form Then the inverse of the matrix is equal to Specially, when , one gets the result of [23].

Proof. Let . Obviously, for . In the case , we obtain . For , we obtain Hence, we get , where is identity matrix. Similarly, we can verify that . Thus, the proof is completed.

Theorem 16. Let be skew circulant matrix; then where Specially, when , one gets the result of [23].

Proof. Let where
Then, we have so , where is a diagonal matrix and is the direct sum of and . If we denote , then we obtain .
Since the last row elements of the matrix are (), then the last row elements of the matrix are ( ), where Hence, it follows from Lemma 15 that letting , then its last row elements are () which are given by the following equations: Hence, we obtain where This completes the proof.

#### 3. Norm and Spread of Skew Circulant Matrix with the Lucas Numbers

Theorem 17. Let be skew circulant matrix; then three kinds of norms of are given by

Proof. By Definition 8 and (12), we have By Definition 8 and (13), we have Thus

Theorem 18. Let be an odd-order alternative skew circulant matrix and let be odd. Then

Proof. By Lemma 3, we have So for all .
Since is odd, is an eigenvalue of ; that is, To sum up, we have
Since all skew circulant matrices are normal, by Lemma 9 and (12), and (52), we have which completes the proof.

Theorem 19. Let be skew circulant matrix; then the bounds for the spread of are

Proof. The trace of , . By (18) and (43), we have Since by (12) and (14), By (19), we have

#### 4. Determinant and Inverse of Skew Left Circulant Matrix with the Lucas Numbers

In this section, let be skew left circulant matrix. By using the obtained conclusions in Section 2, we give a determinant explicit formula for the matrix . Afterwards, we prove that is an invertible matrix for any positive interger . The inverse of the matrix is also presented.

According to Lemmas 5 and 6 and Theorems 13, 14, and 16, we can obtain the following theorems.

Theorem 20. Let be skew left circulant matrix; then where is the th Lucas number.

Theorem 21. Let be skew left circulant matrix; then is an invertible matrix.

Theorem 22. Let be skew left circulant matrix; then where

#### 5. Norm and Spread of Skew Left Circulant Matrix with the Lucas Numbers

Theorem 23. Let be skew left circulant matrix. Then three kinds of norms of are given by

Proof. Using the method in Theorem 17 similarly, the conclusion is obtained.

Theorem 24. Let be an odd-order alternative skew left circulant matrix; then

Proof. According to Lemma 4, for , and So By (66) and (67), we have Since all skew left circulant matrices are symmetrical, by Lemma 9 and (12) and (68), we obtain

Theorem 25. Let be skew left circulant matrix; the bounds for the spread of are where

Proof. Since is a symmetric matrix, by (20), The trace of is, if is odd, By (12), we have Let ; then, by (18), (62), and (74), we obtain If is even, then By (18), (62), and (76), we have So the result follows.

#### 6. Conclusion

We discuss the invertibility of the skew circulant type matrices with any continuous Lucas numbers and present the determinant and the inverse matrices by constructing the transformation matrices. The four kinds of norms and bounds for the spread of these matrices are given, respectively. In [3], a new fast algorithm for optimal design of block digital filters (BDFs) is proposed based on skew circulant matrix. The reason why we focus our attention on skew circulant is to explore the application of skew circulant in the related field in medicine image, image encryption, and real-time tracking. On the basis of existing application situation [4], we conjecture that SVD decomposition of skew circulant matrix will play an important role in CT-perfusion imaging of human brain. On the basis method of [8] and ideas of [5], we will exploit real-time tracking with kernel matrix of skew circulant structure. A novel chaotic image encryption scheme based on the time-delay Lorenz system is presented in [6] with the description of circulant matrix. We will exploit chaotic image encryption algorithm based on skew circulant operation.

#### Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

#### Acknowledgments

This research was supported by the Natural Science Foundation of Shandong Province (Grant no. ZR2011FL017), the National Nature Science Foundation of China (Grant no. F020701), and the AMEP of Linyi University, China.

#### References

1. A. Daher, E. H. Baghious, and G. Burel, â€śFast algorithm for optimal design of block digital filters based on circulant matrices,â€ť IEEE Signal Processing Letters, vol. 15, pp. 637â€“640, 2008. View at: Publisher Site | Google Scholar
2. V. C. Liu and P. P. Vaidyanathan, â€śCirculant and skew-circulant matrices as new normal-form realization of IIR digital filters,â€ť IEEE Transactions on Circuits and Systems, vol. 35, no. 6, pp. 625â€“635, 1988. View at: Publisher Site | Google Scholar | MathSciNet
3. D. Q. Fu, Z. L. Jiang, Y. F. Cui, and S. T. Jhang, â€śA new fastalgorithm for optimal design of block digital filters by skew-cyclic convolution,â€ť IET Signal Processing, p. 6, 2014. View at: Publisher Site | Google Scholar
4. H.-J. Wittsack, A. M. Wohlschläger, E. K. Ritzl et al., â€śCT-perfusion imaging of the human brain: advanced deconvolution analysis using circulant singular value decomposition,â€ť Computerized Medical Imaging and Graphics, vol. 32, no. 1, pp. 67â€“77, 2008. View at: Publisher Site | Google Scholar
5. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, â€śExploiting the circulant structure of tracking-by-detection with kernels,â€ť in Computer Vision—ECCV 2012, vol. 7575 of Lecture Notes in Computer Science, pp. 702â€“715, Springer, Berlin, Germany, 2012. View at: Publisher Site | Google Scholar
6. X. Huang, G. Ye, and K.-W. Wong, â€śChaotic image encryption algorithm based on circulant operation,â€ť Abstract and Applied Analysis, vol. 2013, Article ID 384067, 8 pages, 2013. View at: Publisher Site | Google Scholar | MathSciNet
7. Y. Jing and H. Jafarkhani, â€śDistributed differential space-time coding for wireless relay networks,â€ť IEEE Transactions on Communications, vol. 56, no. 7, pp. 1092â€“1100, 2008. View at: Publisher Site | Google Scholar
8. M. J. Narasimha, â€śLinear convolution using skew-cyclic convolutions,â€ť IEEE Signal Processing Letters, vol. 14, no. 3, pp. 173â€“176, 2007. View at: Publisher Site | Google Scholar
9. T. A. Gulliver and M. Harada, â€śNew nonbinary self-dual codes,â€ť IEEE Transactions on Information Theory, vol. 54, no. 1, pp. 415â€“417, 2008. View at: Publisher Site | Google Scholar | MathSciNet
10. P. J. Davis, Circulant Matrices, John Wiley & Sons, New York, NY, USA, 1979. View at: MathSciNet
11. Z. L. Jiang and Z. X. Zhou, Circulant Matrices, Chengdu Technology University Publishing, Chengdu, China, 1999.
12. D. Bertaccini and M. K. Ng, â€śSkew-circulant preconditioners for systems of LMF-based ODE codes,â€ť in Numerical Analysis and Its Applications, vol. 1988 of Lecture Notes in Computer Science, pp. 93â€“101, Springer, Berlin, Germany, 2001.
13. R. H. Chan and X.-Q. Jin, â€śCirculant and skew-circulant preconditioners for skew-Hermitian type Toeplitz systems,â€ť BIT Numerical Mathematics, vol. 31, no. 4, pp. 632â€“646, 1991.
14. R. H. Chan and K.-P. Ng, â€śToeplitz preconditioners for Hermitian Toeplitz systems,â€ť Linear Algebra and Its Applications, vol. 190, pp. 181â€“208, 1993.
15. T. Huckle, â€śCirculant and skew circulant matrices for solving Toeplitz matrix problems,â€ť SIAM Journal on Matrix Analysis and Applications, vol. 13, no. 3, pp. 767â€“777, 1992.
16. J. N. Lyness and T. Sørevik, â€śFour-dimensional lattice rules generated by skew-circulant matrices,â€ť Mathematics of Computation, vol. 73, no. 245, pp. 279â€“295, 2004.
17. H. Karner, J. Schneid, and C. W. Ueberhuber, â€śSpectral decomposition of real circulant matrices,â€ť Linear Algebra and Its Applications, vol. 367, pp. 301â€“311, 2003.
18. J. Li, Z. L. Jiang, N. Shen, and J. W. Zhou, â€śOn optimal backward perturbation analysis for the linear system with skew circulant coefficient matrix,â€ť Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 707381, 7 pages, 2013. View at: Publisher Site | Google Scholar
19. D. V. Jaiswal, â€śOn determinants involving generalized Fibonacci numbers,â€ť The Fibonacci Quarterly, vol. 7, pp. 319â€“330, 1969.
20. D. A. Lind, â€śA Fibonacci circulant,â€ť The Fibonacci Quarterly, vol. 8, no. 5, pp. 449â€“455, 1970.
21. L. Dazheng, â€śFibonacci-Lucas quasi-cyclic matrices,â€ť The Fibonacci Quarterly, vol. 40, no. 3, pp. 280â€“286, 2002.
22. S.-Q. Shen, J.-M. Cen, and Y. Hao, â€śOn the determinants and inverses of circulant matrices with Fibonacci and Lucas numbers,â€ť Applied Mathematics and Computation, vol. 217, no. 23, pp. 9790â€“9797, 2011.
23. Y. Gao, Z. L. Jiang, and Y. P. Gong, â€śOn the determinants and inverses of skew circulant and skew left circulant matrices with Fibonacci and Lucas numbers,â€ť WSEAS Transactions on Mathematics, vol. 12, no. 4, pp. 472â€“481, 2013. View at: Google Scholar
24. X. Y. Jiang, Y. Gao, and Z. L. Jiang, â€śDeterminantsand inverses of skew and skew left circulant matrices involving the $k$-Fibonacci numbers in communications-I,â€ť Far East Journal of Mathematical Sciences, vol. 76, no. 1, pp. 123â€“137, 2013. View at: Google Scholar
25. X. Y. Jiang, Y. Gao, and Z. L. Jiang, â€śDeterminants and inverses of skew and skew left circulant matrices involving the $k$-Lucas numbers in communications-II,â€ť Far East Journal of Mathematical Sciences, vol. 78, no. 1, pp. 1â€“17, 2013. View at: Google Scholar
26. S. Solak, â€śOn the norms of circulant matrices with the Fibonacci and Lucas numbers,â€ť Applied Mathematics and Computation, vol. 160, no. 1, pp. 125â€“132, 2005.
27. A. İpek, â€śOn the spectral norms of circulant matrices with classical Fibonacci and Lucas numbers entries,â€ť Applied Mathematics and Computation, vol. 217, no. 12, pp. 6011â€“6012, 2011.
28. S. Shen and J. Cen, â€śOn the bounds for the norms of $r$-circulant matrices with the Fibonacci and Lucas numbers,â€ť Applied Mathematics and Computation, vol. 216, no. 10, pp. 2891â€“2897, 2010. View at: Publisher Site | Google Scholar | MathSciNet
29. M. Akbulak and D. Bozkurt, â€śOn the norms of Toeplitz matrices involving Fibonacci and Lucas numbers,â€ť Hacettepe Journal of Mathematics and Statistics, vol. 37, no. 2, pp. 89â€“95, 2008.
30. A. Bose, R. S. Hazra, and K. Saha, â€śSpectral norm of circulant-type matrices,â€ť Journal of Theoretical Probability, vol. 24, no. 2, pp. 479â€“516, 2011.
31. L. Mirsky, â€śThe spread of a matrix,â€ť Mathematika, vol. 3, pp. 127â€“130, 1956.
32. R. Sharma and R. Kumar, â€śRemark on upper bounds for the spread of a matrix,â€ť Linear Algebra and Its Applications, vol. 438, no. 11, pp. 4359â€“4362, 2013.
33. L. Mirsky, â€śInequalities for normal and Hermitian matrices,â€ť Duke Mathematical Journal, vol. 24, no. 4, pp. 591â€“599, 1957.
34. E. R. Barnes and A. J. Hoffman, â€śBounds for the spectrum of normal matrices,â€ť Linear Algebra and Its Applications, vol. 201, pp. 79â€“90, 1994.
35. E. Jiang and X. Zhan, â€śLower bounds for the spread of a Hermitian matrix,â€ť Linear Algebra and Its Applications, vol. 256, pp. 153â€“163, 1997.
36. R. Bhatia and R. Sharma, â€śSome inequalities for positive linear maps,â€ť Linear Algebra and Its Applications, vol. 436, no. 6, pp. 1562â€“1571, 2012.
37. J. Wu, P. Zhang, and W. Liao, â€śUpper bounds for the spread of a matrix,â€ť Linear Algebra and Its Applications, vol. 437, no. 11, pp. 2813â€“2822, 2012.
38. C. R. Johnson, R. Kumar, and H. Wolkowicz, â€śLower bounds for the spread of a matrix,â€ť Linear Algebra and Its Applications, vol. 71, pp. 161â€“173, 1985.

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