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] :

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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
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The singular values of the matrix A is
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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
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The following inequality holds,
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Define the maximum column lenght norm
, and the maximum row lenght norm
of any matrix A by
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and
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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
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Then we have,
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hence, when
we obtain
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that is
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On the other hand, let the matrices B and C as
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and
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such that
. Then
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and
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We have
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when
we also obtain
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that is
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On the other hand, let the matrices B and C as
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and
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such that
. Then
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and
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We have
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¢
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
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then we have
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hence when
we obtain
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that is
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On the other hand let matrices B and C be as
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and
![]()
such that
. Then
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and
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We have
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when
we also obtain
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that is
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On the other hand, let matrices B and C be as
![]()
and
![]()
such that
. Then
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and
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We have
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¢
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
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*Corresponding author.