Keywords
Generalized inverses, Secondary generalized inverses, Secondary transpose, EP matrices
This article is included in the Manipal Academy of Higher Education gateway.
The concept of secondary range symmetric matrices are introduced here. Some characterizations as well as the equivalent conditions for a range symmetric matrix to be secondary range symmetric matrix is given. The idea of range symmetric matrices, range symmetric matrices over Minkowski space and secondary range symmetric matrices are different, and is depicted with the help of suitable examples. Finally, a necessary and sufficient condition for a secondary range symmetric matrix to have a secondary generalized inverse has been obtained.
Generalized inverses, Secondary generalized inverses, Secondary transpose, EP matrices
The theory of symmetric matrices as well as range symmetric matrices are well known in literature. A matrix is said to be EP (or range symmetric), whenver the range space of the matrix is equal to the range space of its conjugate transpose. In other words, matrix is EP whenever its null space is same as that of the null space of its conjugate transpose. Ballantine1 has studied about the product of two EP matrices of specific rank to be again an EP matrix. In2 new characterizations of EP matrices are given. Also, weighted EP matrix is defined and characterized. Meenakshi3 extended the concept of range symmetric matrices over Minkowski space. In 2014, the same author defined range symmetric matrices in indefinite inner product space.4
For an matrix , the secondary transpose is related to transpose of the matrix by the relation . Here, the matrix has non zero unitary entries only on the secondary diagonal. For a matrix with complex entries, the secondary transpose will be renamed as secondary conjugate transpose and is given by .
(Ref. 5) Let . Then the secondary transpose (or secondary conjugate transpose in the case of complex matrices) of denoted by and is defined as , where , .
Throughout this article we assume to be a permutation matrix with units in the secondary diagonal. Also, represents the null space of the matrix . The column space and rank of are denoted by respectively. The set of matrices of order over the field of real numbers is denoted by .
The concept of secondary conjugate transpose is gaining importance in recent years. Shenoy6 has defined Outer Theta inverse by combining the outer inverse and secondary transpose of a matrix . Drazin-Theta matix 7 is a new class of generalized inverse introduced for a square matrix of index m. One can refer8 for the extension of these inverses over rectangular matrices. R. Vijayakumar9 introduced the concept of secondary generalized inverse with the help of secondary transpose of a matrix. This concept is similar to Moore Penrose inverse. But unlike Moore penrose inverse, existence of s-g inverse is not assured in general. In Ref. 10, a necessary and sufficient conditions of existence of s-g invese is given. In the same article, a few characterizations and a determinantal formula for s-g inverse also has been discussed. In 2009, Krishnamoorthy and Vijayakumar11 has defined the concept of s-normal matrices with the help of secondary transopse for a class of complex square matrices. Jayashree12 has defined secondary k-range symmetric fuzzy matrices. Its relation with s-range symmetric fuzzy matrices, k-range fuzzy symmetric matrices and EP matrices are defined.
In this article, we define secondary range symmetric matrices. Several equivalent conditions for a matrix to be secondary range symmetric, is obtained here. Also, the existence of secondary generalized inverse of a secondary range symmetric matrix is discussed.
Below are some useful deifnitions and results related to secondary conjugate transpose.
(Ref. 13) Let . Then the conjugate secondary transpose of denoted by and is defined as where .
(Ref. 13) A matrix is said to be secondary normal (s-normal) if .
(Ref. 10) is said to be secondary generalized inverse of if
and and are -symmetric.(Ref. 10) Given an matrix . The following statements are equivalent.
(Ref. 14) A matrix is said to be EP (or range symmetric) if .
Meenakshi3 has defined EP in Minkowski space and has given equivalent conditions for a matrix to be range symmetric.
(Ref. 3) A matrix is said to be range symmetric in Minkowski space if and only if .
Here represents the Minkowski adjoint given by where is the Minkowski metric tensor.
In this section, we define secondary range symmetric matrices which is analogous to that of range symmetric matrices. Some equivalent conditions for a matrix to be range symmetric is also given here.
is secondary right (left) normalized g inverse of if , and is -symmetric. ( is -symmetric).
Consider . The s-transpose of is defined as where , .
A matrix secondary range symmetric if and only if
Let . Then the following conditions are equivalent.
Hence holds true.
This proves the equivalence of and .
Hence and are equivalent.
Hence .
Thus equivalence of and are proved.
Thus equivalence of and are proved.
Thus the equivalence of and is proved.
From the following example, it is clear that EP matrices in Minkowski space defined by Meenakshi3 and secondary range symmetric matrices are two different concepts.
Consider a matrix where . Here, is secondary normal as well as . The matrix is secondary range symmetric. However, the matrix is not range symmetric in Minkowski’s space since . It is clear that .
Let and . Clearly is not secondary range symmetric.
Observe that is range symmetric in Minkowski space. Since, so that .
Note that .
A necessary condition for a matrix to be a s-EP (secondary range symmetric) is proved here.
Let . If is secondary normal and , then is secondary range symmetric.
Since is secondary normal, . Hence which implies . Thus is secondary range symmetric.
A relation connecting range symmetric and secondary range symmetric matrices is given below:
Let . Then any two of the following conditions imply the third one.
Since is EP, . By Theorem 0.2, as is secondary range symmetric. Hence is secondary EP .
Since is s-EP, by Theorem 0.2, is range symmetric. Hence . Also, By (3), from which it follows that . Hence is range symmetric. Thus (1) holds.
For any square complex matrix , there exists unique - symmetric matrices such that where and . In the following theorem, an equivalent condition for a matrix to be secondary range symmetric is obtained interms of , the -symmetric part of .
For , is secondary range symmetric if and only if where is the -symmetric part of .
If is secondary range symmetric, then . For , and . Hence . Thus, , then and hence . Therefore . Thus . Since, both and are -symmetric, they are secondary range symmetric.
andNow, and . Therefore, . Thus is secondary range symmetric.
We shall discuss the existence of secondary generalized inverse inverse of a secondary range symmetric matrix. First, we shall prove certain lemmas, to simplify the proof of the main result.
For an matrix , if exists, then .
For an matrix , if exists, then is the projection on and is the projection on .
if and only if . By definition 2, being -symmetric, idempotent is the projection on . Similarly, if and only if and is -symmetric and idempotent. Hence is the projection on .
For an matrix , the following are equivalent:
. Since and is secondary symmetric, by using Theorem 0.2 we have, and . Thus . Hence by Thoerem 0.1, it follows that exists, By Lemma 1 and Theorem 0.2, . Hence is range symmetric. Thus (2) holds.
. Since exists, by Lemma 1, , by Theorem 0.2, is secondary range symmetric which implies that . Hence . By Lemma 2, it follows that , hence . By definition 2, , is -symmetric, idempotent and ; hence and , which implies . Thus holds.
. Since, is -symmetric and idempotent, , by lemma 1.1, exists and implies is the projection on . For all reflexive g-inverses of , . Since is -symmetric and idempotent, is -symmetric. Hence by definition 7, exists and which implies . By hypothesis . Therefore . Thus both and are -symmetric. By definition 2, exists and . By taking secondary transpose on , we get . and . Therefore . By theorem 0.2, is secondary ramge symmetric. , . Thus . Thus (1) holds. Hence the theorem.
Consider an secondary range symmetric matrix . Then exists if and only if .
Since is secondary range symmetric and , the existence of follows from Thereom 0.6. Conversly, if is secondary range symmetric, and exists, then by Theorem 0.1 and by Theorem 0.2, . Hence .
As an extension of this work, one can think of defining weighted secondary EP matrices and its characteriations. Also, extending secodary range symmetric matrix to indefinite inner prodcut spaces will open up a new area of research.
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Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Yes
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Not applicable
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: The paper tried to supply new results with avoid any mistake.
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Yes
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Combinatorial matrices, Discrete Mathematics, computer science
Is the work clearly and accurately presented and does it cite the current literature?
No
Is the study design appropriate and is the work technically sound?
Yes
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Not applicable
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Yes
References
1. Johnson P, Vinoth A: Product and factorization of hypo-EP operators. Special Matrices. 2018; 6 (1): 376-382 Publisher Full TextCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: Functional Analysis
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