Journal of Function Spaces

Volume 2014 (2014), Article ID 672398, 5 pages

http://dx.doi.org/10.1155/2014/672398

## The Identical Estimates of Spectral Norms for Circulant Matrices with Binomial Coefficients Combined with Fibonacci Numbers and Lucas Numbers Entries

Department of Mathematics, Linyi University, Linyi 276005, China

Received 1 November 2013; Accepted 23 January 2014; Published 27 February 2014

Academic Editor: Yuming Xing

Copyright © 2014 Jianwei Zhou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Improved estimates for spectral norms of circulant matrices are investigated, and the entries are binomial coefficients combined with either Fibonacci numbers or Lucas numbers. Employing the properties of given circulant matrices, this paper improves the inequalities for their spectral norms, and gets corresponding identities of spectral norms. Moreover, by some well-known identities, the explicit identities for spectral norms are obtained. Some numerical tests are listed to verify the results.

#### 1. Introduction

Circulant matrices have connection to physics, signal and image processing, probability, statistics, numerical analysis, algebraic coding theory, and many other areas. There are lots of examples from statistical signal processing and information theory that illustrate the application of the circulant matrices, which emphasize how the asymptotic eigenvalue distribution theorem allows one to evaluate results for processes (for the details please refer to [1–3] and the reference therein). Meanwhile a real circulant stochastic process can be described with autocovariance matrices, which are subjected to a cyclical permutation. With the help of autocovariance circulant matrices, it is easy to provide derivations of some results that are central to the analysis of statistical periodograms and empirical spectral density functions (see [4]).

In past decades, the estimates for spectral norms of matrices have been investigated in lots of literatures. Moreover, the determinants and inverses of circulant matrices are stated in many articles. The norms of circulant matrices play an important role in analysing the process of statistics, numerical analysis, and many other problems (for more details, please refer to [3, 5–10] and the reference therein). Bryc and Sethuraman [11] investigated the maximum eigenvalue for circulant matrices. Solak [7] obtained lower and upper bounds for the spectral norm of circulant matrices, where the entries are classical Fibonacci numbers. İpek [8] establishes spectral norms of circulant matrices with Fibonacci and Lucas numbers. Furthermore, circulant matrices take up an important status in stochastic calculus, Meckes [12, 13] gave some results on the spectral norm of a special random Toeplitz matrix and random circulant matrices, Mehta [14] made a deep discussion on random circulant matrices.

The outline of this paper is as follows. In Section 2, we state some preliminaries and recall some well-known results. In Section 3, we focus on the identities of estimations for spectral norms. In Section 4, we present various numerical examples to exhibit the accuracy and efficiency of our results. Finally, we summarise this paper and illustrate our future work.

#### 2. Preliminaries

The Fibonacci and Lucas sequences and are defined by the recurrence relations: with , , , and , respectively.

Obviously, the Fibonacci and Lucas sequences are listed in the following sequence: and their corresponding Binet forms are (see [15])

Now, we recall that, for there hold the following estimates: For the details please refer to [7].

There are lots of identities for Fibonacci numbers and Lucas numbers combined with Binomial coefficients (for more details please refer to [8, 16–18] and the reference therein). In this paper, we focus on the following identities: Furthermore, for all , there hold the following identities:

*Definition 1 (see [19]). *A circulant matrix is an complex matrix with the following form:

The first row of is and its th row is obtained by giving its th row a right circular shift by one positions.

*Definition 2 (see [3]). *The spectral norm of a matrix with complex entries is the square root of the largest eigenvalue of the positive semidefinite matrix :
where denotes the conjugate transpose of . Therefore if is an real symmetric matrix or is a normal matrix, then
where are the eigenvalues of .

#### 3. The Identities of Estimations for Spectral Norms

We give the main theorems of this paper in the following parts.

Theorem 3. *Let be as the matrix in (9), and let the first row of be . Then one has
*

*Proof. *Combining with Definition 2, the spectral radius of is equal to its spectral norm, where we used the fact that is normal. Moreover, by the irreducible and entrywise nonnegative properties, we deduce that is equal to its Perron value. Denote by an -dimensional column vector. There holds

Obviously, is an eigenvalue of associated with the positive eigenvector , which is the Perron value of . Employing the first identity in (6), we have
This completes the proof.

*With the same approach, we obtain the following corollary.*

*Corollary 4. Let be as the matrix in (9), and let the first row of be . Then one has the following identity:
*

*Theorem 5. Let be with the form as (9). For all , if the first row of is , then one obtains
*

*Proof. *Following the same techniques of the above theorem and combining with the fact that is irreducible and entrywise nonnegative, we declare that the spectral norm of is equal to its Perron value. Let . Then

Obviously, we declare that is an eigenvalue of associated with . With simple analysis, we obtain that is equal to the Perron value of . Combining with the third identity of binomial coefficients and Fibonacci numbers in (6), we obtain
which completes the proof.

*Similarly, there holds the following corollary.*

*Corollary 6. Let be as the matrix in (9). For all , the first row of is ; then
*

*Now, we are at the point to recall the following lemma to verify the identities of spectral norms with other approaches.*

*Lemma 7 (see [3]). Let be a nonnegative matrix. If the column sums of are equal, then
where and denotes the maximum column sum matrix norm.*

*Theorem 8. Let be with the form as (9) and let the first row of be . Then one deduces the following identity:
where .*

*Proof. *Obviously, the circulant matrix is normal; with the results of Definition 2, we declare that the spectral radius of is equal to ; that is, . Furthermore, applying entrywise nonnegative properties and column sum of are certain constant , which is described in (7). By Lemma 7, we obtain

Employing the identities of Fibonacci numbers and Binomial coefficients in (7), we have
This completes the proof.

*Furthermore, we give the following corollary without proofs, which can be proved with the same approaches as the above theorem.*

*Corollary 9. Let be as the matrix in (9). For all , the first row of is ; then we have the following identity:
*

*4. Numerical Examples*

*4. Numerical Examples**In this section, we give some examples to verify our identities in the above theorems and corollaries.*

*Example 1. *In this example, we give the numerical results for in Table 1.

*Example 2. *For simplicity, let . We give the numerical results for and in Table 2.

With the data in Tables 1 and 2, we declare that the identity for the spectral norm of holds.

*5. Conclusion*

*5. Conclusion**This paper had discussed the identical estimates of spectral norms for some circulant matrices, which are listed by explicit formulations. In the future, we are going to investigate the determinants, inverses of circulant matrices with certain entries, and, inspired by [6], we will investigate the properties of -circulant matrices. Particularly worth mentioning is the fact that, for the -circulant matrix, we had some numerical results to prove the fact that the same identical estimates hold precisely, and we will concern on the theoretical confirmation in part of the future work.*

*Conflict of Interests*

*Conflict of Interests**The author declares that there is no conflict of interests regarding the publication of this paper.*

*Acknowledgments*

*Acknowledgments**The author thanks Professor Z. L. Jiang for valuable discussions and suggestions and wishes to express sincere thanks to referees for their useful suggestions and comments. This work is partly supported by National Natural Science Foundation of China (Grant no. 11201212), Promotive Research Fund for Excellent Young and Middle-Aged Scientists of Shandong Province (Grant no. BS2012DX004), and the AMEP of Linyi University.*

*References*

*References*

- W.-S. Chou, B.-S. Du, and P. J.-S. Shiue, “A note on circulant transition matrices in Markov chains,”
*Linear Algebra and Its Applications*, vol. 429, no. 7, pp. 1699–1704, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - R. M. Gray and L. D. Davisson,
*An Introduction to Statistical Signal Processing*, Cambridge University Press, London, UK, 2005. - R. A. Horn and C. R. Johnson,
*Matrix Analysis*, Cambridge University Press, Cambridge, UK, 1985. View at MathSciNet - D. S. G. Pollock, “Circulant matrices and time-series analysis,”
*Working Paper No.*442, Queen Mary & Westfield College, 2000. View at Google Scholar - A. Bose, R. S. Hazra, and K. Saha, “Spectral norm of circulant-type matrices,”
*Journal of Theoretical Probability*, vol. 24, no. 2, pp. 479–516, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - E. Ngondiep, S. Serra-Capizzano, and D. Sesana, “Spectral features and asymptotic properties for $g$-circulants and $g$-Toeplitz sequences,”
*SIAM Journal on Matrix Analysis and Applications*, vol. 31, no. 4, pp. 1663–1687, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - S. Solak, “On the norms of circulant matrices with the Fibonacci and Lucas numbers,”
*Applied Mathematics and Computation*, vol. 160, no. 1, pp. 125–132, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - A. İpek, “On the spectral norms of circulant matrices with classical Fibonacci and Lucas numbers entries,”
*Applied Mathematics and Computation*, vol. 217, no. 12, pp. 6011–6012, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - J. W. Zhou and Z. L. Jiang, “Spectral norms of circulant-type matri-ces with Binomial coefficients and Harmonic numbers,”
*International Journal of Computational Methods*, vol. 11, no. 5, Article ID 1350076, 14 pages, 2014. View at Google Scholar - J. W. Zhou and Z. L. Jiang, “Spectral norms of circulant and Skew-circulant matrices with Binomial coefficients entries,” in
*Proceedings of the 9th International Symposium on Linear Drives for Industry Applications*, vol. 271 of*Lecture Notes in Electrical Engineering*, pp. 219–224, Springer, Berlin, Germany, 2014. View at Publisher · View at Google Scholar - W. Bryc and S. Sethuraman, “A remark on the maximum eigenvalue for circulant matrices,” in
*High Dimensional Probability V: The Luminy Volume*, vol. 5, pp. 179–184, Institute of Mathematical Statistics Collections, Beachwood, Ohio, USA, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - M. W. Meckes, “On the spectral norm of a random Toeplitz matrix,”
*Electronic Communications in Probability*, vol. 12, pp. 315–325, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus - M. W. Meckes, “Some results on random circulant matrices,” in
*High Dimensional Probability V: The Luminy Volume*, vol. 5, pp. 213–223, Institute of Mathematical Statistics Collections, Beachwood, Ohio, USA, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - M. L. Mehta,
*Random Matrices*, vol. 142 of*Pure and Applied Mathematics*, Elsevier/Academic Press, Amsterdam, The Netherlands, 3rd edition, 2004. View at MathSciNet - E. G. Kocer, N. Tuglu, and A. Stakhov, “On the $m$-extension of the Fibonacci and Lucas $p$-numbers,”
*Chaos, Solitons and Fractals*, vol. 40, no. 4, pp. 1890–1906, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - M. Akbulak and D. Bozkurt, “On the norms of Toeplitz matrices involving Fibonacci and Lucas numbers,”
*Hacettepe Journal of Mathematics and Statistics*, vol. 37, no. 2, pp. 89–95, 2008. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - M. Benoumhani, “A sequence of Binomial coefficients related to Lucas and Fibonacci numbers,”
*Journal of Integer Sequences*, vol. 6, no. 2, pp. 1–10, 2003. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - R. Melham, “Sums involving Fibonacci and Pell numbers,”
*Portugaliae Mathematica*, vol. 56, no. 3, pp. 309–317, 1999. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - W. T. Stallings and T. L. Boullion, “The pseudoinverse of an $r$-circulant matrix,”
*Proceedings of the American Mathematical Society*, vol. 34, no. 2, pp. 385–388, 1972. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet

*
*