Received 14 December 2015; accepted 30 May 2016; published 2 June 2016
1. Introduction
Toeplitz matrices arise in many different theoretical and applicative fields, in the mathematical modeling of all the problems where some sort of shift invariance occurs in terms of space or of time. As in computation of spline functions, time series analysis, signal and image processing, queueing theory, polynomial and power series computations and many other areas, typical problems modelled by Toeplitz matrices are the numerical solution of certain differential and integral equations [1] - [5] .
Lots of article have been written so far, which concern estimates for spectral norms of Toeplitz matrices, which have connections with signal and image processing, time series analysis and many other problems [6] - [8] . Akbulak and Bozkurt found lower and upper bounds for the spectral norms of Toeplitz matrices with classical Fibonacci and Lucas numbers entries in [9] . Shen gave upper and lower bounds for the spectral norms of Toeplitz matrices with k-Fibonacci and k-Lucas numbers entries in [10] .
In this paper, we derive expressions of spectral norms for r-Toeplitz matrices. We explain some preliminaries and well-known results. We thicken the identities of estimations for spectral norms of r-Toeplitz matrices.
2. Preliminaries
The Fibonacci and Lucas sequences and are defined by the recurrence relations
and
The rule can be used to extend the sequence backwards. Hence
and
If start from, then the Fibonacci and Lucas sequence are given by
The following sum formulas the Fibonacci and Lucas numbers are well known [11] [12] :
A matrix is called a r-Toeplitz matrix if it is of the form
(1)
Obviously, the r-Toeplitz matrix T is determined by parameter r and its first row elements, thus we denote. Especially, let, the matrix T is called a Toeplitz matrix.
A matrix is called a symmetric r-Toeplitz matrix if it is of the form
(2)
Obviously, the symmetric r-Toeplitz matrix T is determined by parameter r and its last row elements, thus we denote . Especially, let, the matrix T is called a Toeplitz matrix.
The Euclidean norm of the matrix A is defined as
The singular values of the matrix A is
where is an eigenvalue of and is conjugate transpose of matrix A. For a square matrix A, the square roots of the maximum eigenvalues of are called the spectral norm of A. The spectral norm of the matrix A is
The following inequality holds,
Define the maximum column lenght norm, and the maximum row lenght norm of any matrix A by
and
respectively. Let A, B and C be matrices. If then
[13] .
Theorem 1 [9] . Let be a Toeplitz matrix satisfying, then
where is the spectral norm and denotes the nth Fibonacci number.
Theorem 2 [9] . Let be a Toeplitz matrix satisfying, then
where is the spectral norm and denotes the nth Lucas number.
3. Result and Discussion
Theorem 3. Let be a r-Toeplitz matrix satisfying, where.
・
・
where is the spectral norm and denotes the nth Fibonacci number.
Proof. The matrix A is of the form
Then we have,
hence, when we obtain
that is
On the other hand, let the matrices B and C as
and
such that. Then
and
We have
when we also obtain
that is
On the other hand, let the matrices B and C as
and
such that. Then
and
We have
¢
Thus, the proof is completed.
Corollary 4. Let be a symmetric r-Toeplitz matrix, where r C, then
・
・
where is the spectral norm and denotes the nth Fibonacci number.
Proof. Owing to the fact that the sum of all elements squares are equal in matrices (1) and (2), the proof is concluded analogously in the proof of previous theorem. ¢
Theorem 5. Let be a r-Toeplitz matrix satisfying, where.
・
・
where is the spectral norm and denotes the nth Lucas number.
Proof. The matrix A is of the form
then we have
hence when we obtain
that is
On the other hand let matrices B and C be as
and
such that. Then
and
We have
when we also obtain
that is
On the other hand, let matrices B and C be as
and
such that. Then
and
We have
¢
Thus, the proof is completed.
Corollary 6. Let be a symmetric r-Toeplitz matrix, where, then
・
・
where is the spectral norm and denotes the nth Lucas number.
Proof. Owing to the fact that the sum of all elements squares are equal in matrices (1) and (2), the proof is concluded analogously in the proof of previous theorem. ¢
4. Numarical Examples
Example 7. Let be a r-Toeplitz matrix, in which denotes the Fibonacci number, where. From Table 1, it is easy to find that upper bounds for the spectral norm, of Theorem 3 are more sharper than Theorem 1 (see Table 1).
Example 8. Let be a r-Toeplitz matrix, in which denotes the Lucas number, where. From Table 2, it is easy to find that upper bounds for the spectral norm, of Theorem 5 are more sharper than Theorem 2, when n ≥ 2 (see Table 2).
NOTES
*Corresponding author.