Research Article  Open Access
Fine Spectra of Upper Triangular TripleBand Matrices over the Sequence Space ()
Abstract
The fine spectra of lower triangular tripleband matrices have been examined by several authors (e.g., Akhmedov (2006), Başar (2007), and Furken et al. (2010)). Here we determine the fine spectra of upper triangular tripleband matrices over the sequence space . The operator on sequence space on is defined by , where , with . In this paper we have obtained the results on the spectrum and point spectrum for the operator on the sequence space . Further, the results on continuous spectrum, residual spectrum, and fine spectrum of the operator on the sequence space are also derived. Additionally, we give the approximate point spectrum, defect spectrum, and compression spectrum of the matrix operator over the space and we give some applications.
1. Introduction
In functional analysis, the spectrum of an operator generalizes the notion of eigenvalues for matrices. The spectrum of an operator over a Banach space is partitioned into three parts, which are the point spectrum, the continuous spectrum, and the residual spectrum. The calculation of these three parts of the spectrum of an operator is called calculating the fine spectrum of the operator.
Over the years and different names the spectrum and fine spectra of linear operators defined by some triangle matrices over certain sequence spaces were studied.
By we denote the space of all complexvalued sequences. Any vector subspace of is called a sequence space. We write , , , and for the spaces of all bounded, convergent, null, and bounded variation sequences, respectively, which are the Banach spaces with the supnorm and , respectively, where . Also by and we denote the spaces of all absolutely summable and absolutely summable sequences, which are the Banach spaces with the norm , respectively, where .
Several authors studied the spectrum and fine spectrum of linear operators defined by some triangle matrices over some sequence spaces. We introduce knowledge in the existing literature concerning the spectrum and the fine spectrum. Cesàro operator of order one on the sequence space was studied by Gonzàlez [1], where . Also, weighted mean matrices of operators on have been investigated by Cartlidge [2]. The spectrum of the Cesàro operator of order one on the sequence spaces and were investigated by Okutoyi [3, 4]. The spectrum and fine spectrum of the Rally operators on the sequence space were examined by Yıldırım [5]. The fine spectrum of the difference operator over the sequence spaces and was studied by Altay and Başar [6]. The same authors also worked out the fine spectrum of the generalized difference operator over and , in [7]. Recently, the fine spectra of the difference operator over the sequence spaces and have been studied by Akhmedov and Başar [8, 9], where is the space consisting of the sequences such that and introduced by Başar and Altay [10] with . In the recent paper, Furkan et al. [11] have studied the fine spectrum of over the sequence spaces and with , where is a lower triangular tripleband matrix. Later, Karakaya and Altun have determined the fine spectra of upper triangular doubleband matrices over the sequence spaces and , in [12]. Quite recently, Karaisa [13] has determined the fine spectrum of the generalized difference operator , defined as an upper triangular doubleband matrix with the convergent sequences and having certain properties, over the sequence space , where .
In this paper, we study the fine spectrum of the generalized difference operator defined by a triple sequential band matrix acting on the sequence space (), with respect to Goldberg's classification. Additionally, we give the approximate point spectrum and defect spectrum and give some applications.
2. Preliminaries, Background, and Notation
Let and be two Banach spaces and be a bounded linear operator. By we denote range of , that is, By we also denote the set of all bounded linear operators on into itself. If then the adjoint of is a bounded linear operator on the dual of defined by for all and .
Let be a complex normed space and be a linear operator with domain . With we associate the operator , where is a complex number and is the identity operator on . If has an inverse which is linear, we denote it by , that is, and call it the resolvent operator of .
Many properties of and depend on , and spectral theory is concerned with those properties. For instance, we shall be interested in the set of all in the complex plane such that exists. The boundedness of is another property that will be essential. We shall also ask for what the domain of is dense in , to name just a few aspects For our investigation of , , and , we need some basic concepts in spectral theory which are given as follows (see [14, pp. 370371]).
Let be a complex normed space and be a linear operator with domain . A regular value of is a complex number such that(R1) exists,(R2) is bounded,(R3) is defined on a set which is dense in .The resolvent set of is the set of all regular values of . Its complement in the complex plane is called the spectrum of . Furthermore, the spectrum is partitioned into three disjoint sets as follows. The point spectrum is the set such that does not exist. is called an eigenvalue of . The continuous spectrum is the set such that exists and satisfies (R3) but not (R2). The residual spectrum is the set such that exists but does not satisfy (R3).
In this section, following Appell et al. [15], we define the three more subdivisions of the spectrum called the approximate point spectrum, defect spectrum, and compression spectrum.
Given a bounded linear operator in a Banach space , we call a sequence in as a Weyl sequence for if and , as .
In what follows, we call the set the approximate point spectrum of . Moreover, the subspectrum is called defect spectrum of .
The two subspectra given by (3) and (4) form a (not necessarily disjoint) subdivisions of the spectrum. There is another subspectrum which is often called compression spectrum in the literature.
By the definitions given above, we can illustrate the subdivisions of spectrum in Table 1.

From Goldberg [16] if , a Banach space, then there are three possibilities for :(I),(II), (III),and three possibilities for :(1) exists and is continuous,(2) exists but is discontinuous,(3) does not exist.
If these possibilities are combined in all possible ways, nine different states are created. These are labelled by , , , , , , , and . If is a complex number such that or , then is in the resolvent set of . The further classification gives rise to the fine spectrum of . If an operator is in state , for example, then and exists but is discontinuous and we write .
Let and be two sequence spaces and let be an infinite matrix of real or complex numbers , where . Then, we say that defines a matrix mapping from into and we denote it by writing if for every sequence the sequence , the transform of is in , where By , we denote the class of all matrices such that . Thus, if and only if the series on the right side of (7) converges for each and every , and we have for all .
Proposition 1 (see [15, Proposition 1.3, p. 28]). Spectra and subspectra of an operator and its adjoint are related by the following relations:(a),(b), (c), (d), (e),(f), (g).
The relations (c)–(f) show that the approximate point spectrum is in a certain sense dual to defect spectrum and the point spectrum dual to the compression spectrum.
The equality (g) implies, in particular, that if is a Hilbert space and is normal. Roughly speaking, this shows that normal (in particular, selfadjoint) operators on Hilbert spaces are most similar to matrices in finite dimensional spaces (see [15]).
Lemma 2 (see [16, p. 60]). The adjoint operator of is onto if and only if has a bounded inverse.
Lemma 3 (see [16, p. 59]). has a dense range if and only if is one to one.
Our main focus in this paper is on the tripleband matrix , where We assume here and after that and are complex parameters which do not simultaneously vanish. We introduce the introduce the operator from to itself by
3. Fine Spectra of Upper Triangular TripleBand Matrices over the Sequence Space ()
In this section, we prove that the operator is a bounded linear operator and compute its norm. We essentially emphasize the fine spectrum of the operator in the case .
Theorem 4. The operator is a bounded linear operator and
Proof. Since the linearity of the operator is trivial, so it is omitted. Let us take . Then and observe that which gives the fact that Let , where . Then, since , and , it is easy to see by triangle inequality that which leads us to the result that Therefore, by combining the inequalities (12) and (14) we see that (10) holds which completes the proof.
If is a bounded matrix operator with the matrix , then it is known that the adjoint operator is defined by the transpose of the matrix . The dual space of is isomorphic to , where .
Before giving the main theorem of this section, we should note the following remark. In this work, here and in what follows, if is a complex number, then by we always mean the square root of with a nonnegative real part. If , then represents the square root of with . The same results are obtained if represents the other square root.
Theorem 5. Let be a complex number such that and define the set by Then, .
Proof. Let . Then, by solving the equation for in terms of , we obtain
and if we denote , , and , we have
Now we must find . We have and if we use relation (18), we have
This implies that
In fact this sequence is obtained recursively by letting
The characteristic polynomial of the recurrence relation is . There are two cases.
Case 1. If whose roots are
elementary calculation on recurrent sequence gives that
In this case . Assume that . So we have
Since for any , we must have
It follows that . Now, for we can see that
for all . Taking limit on the inequality (26) as , we get
Thus for , is onto and by Lemma 2, has a bounded inverse. This means that
Case 2. If , a calculation on recurrent sequence gives that
Now, for we can see that
for all . Taking limit on the inequality (30) as , we obtain that
is convergent, since . Thus for , is onto and by Lemma 2, has a bounded inverse. This means that
Theorem 6. .
Proof. Consider with in . Then, by solving the system of linear equations we find that if and if which contradicts . If is the first nonzero entry of the sequence and , then we get which implies which contradicts the assumption . Hence, the equation has no solution .
Theorem 7. , where .
Proof. Let for . Then, by solving the system of linear equations
and we have
Assume that . Then, we choose and . We show that for all . Since are roots of the characteristic equation , we must have
Combining the fact with relation (35), we can see that
The same result may be obtained in case . Now , since . This shows that .
Now we assume that , that is, . We must show that . Therefore we obtain from the relation (35) that
Now we examine three cases.
Case 1 (). In this case we have and
Then, we have
Now, if , then we have which is not in . Otherwise,
Case 2 (). In this case we have and using the formula
we obtain that
which leads to
Case 3 (). In this case we have and so we have . Assume that . This implies that and . Thus we again derive (35)
Since , we must have . But this implies that , a contradiction which means that . Thus . This completes the proof.
Theorem 8. .
Proof. By Proposition 1, . Since by Theorem 6, This completes the proof.
Theorem 9. Let be a complex number such that and define the set by Then, .
Proof. By Theorem 7, we get Since the spectrum of any bounded operator is closed, we have and again from Theorems 5, 7, and 8, Combining (49) and (50), we obtain that , where is defined by (47).
Theorem 10. , where .
Proof. Because the parts , , and are pairwise disjoint sets and the union of these sets is , the proof immediately follows from Theorems 7, 8, and 9.
Theorem 11. If , .
Proof. From Theorem 7, . Thus, does not exist. By Theorem 6 is one to one, so has a dense range in by Lemma 3.
Theorem 12. The following statements hold:(i), (ii), (iii).
Proof. (i) Since from Table 1,
we have by Theorem 8
Hence
(ii) Since the following equality
holds from Table 1, we derive by Theorems 8 and 11 that .
(iii) From Table 1, we have
By Theorem 6 it is immediate that .
4. Fine Spectra of Upper Triangular TripleBand Matrices over the Sequence Space ()
In the present section, we determine the fine spectrum of the operator in case . We quote some lemmas which are needed in proving the theorems given in Section 4.
Lemma 13 (see [17, p. 253, Theorem 34.16]). The matrix gives rise to a bounded linear operator from to itself if and only if the supremum of norms of the columns of is bounded.
Lemma 14 (see [17, p. 245, Theorem 34.3]). The matrix gives rise to a bounded linear operator from to itself if and only if the supremum of norms of the rows of is bounded.
Lemma 15 (see [17, p. 254, Theorem 34.18]). Let and . Then, .
Theorem 16. The operator is a bounded linear operator and
Proof. Since the linearity of the operator is not hard, we omit the details. Now we prove that (56) holds for the operator on the space . It is trivial that for . Therefore, we have which implies that Let , where . Then, since , , and , it is easy to see by Minkowski's inequality that which leads us to the result that Therefore, by combining the inequalities in (58) and (59) we have (56), as desired.
If is a bounded matrix operator with the matrix , then it is known that the adjoint operator is defined by the transpose of the matrix . The dual space of is isomorphic to , where .
Theorem 17. Let be a complex number such that and define the set by Then, .
Proof. We will show that is onto, for . Thus, for every , we find . is a triangle so it has an inverse. Also equation gives . It is sufficient to show that . We calculate that as follows: where It is known that from Theorem 5 Now, we show that , for . By Theorem 5, we know that if , . We assume that and , since