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Row Hadamard majorization on 𝐌 m , n

Abbas AskarizadehAli Armandnejad — 2021

Czechoslovak Mathematical Journal

An m × n matrix R with nonnegative entries is called row stochastic if the sum of entries on every row of R is 1. Let 𝐌 m , n be the set of all m × n real matrices. For A , B 𝐌 m , n , we say that A is row Hadamard majorized by B (denoted by A R H B ) if there exists an m × n row stochastic matrix R such that A = R B , where X Y is the Hadamard product (entrywise product) of matrices X , Y 𝐌 m , n . In this paper, we consider the concept of row Hadamard majorization as a relation on 𝐌 m , n and characterize the structure of all linear operators T : 𝐌 m , n 𝐌 m , n preserving (or...

On row-sum majorization

Farzaneh AkbarzadehAli Armandnejad — 2019

Czechoslovak Mathematical Journal

Let 𝕄 n , m be the set of all n × m real or complex matrices. For A , B 𝕄 n , m , we say that A is row-sum majorized by B (written as A rs B ) if R ( A ) R ( B ) , where R ( A ) is the row sum vector of A and is the classical majorization on n . In the present paper, the structure of all linear operators T : 𝕄 n , m 𝕄 n , m preserving or strongly preserving row-sum majorization is characterized. Also we consider the concepts of even and circulant majorization on n and then find the linear preservers of row-sum majorization of these relations on 𝕄 n , m .

Linear preservers of row-dense matrices

Sara M. MotlaghianAli ArmandnejadFrank J. Hall — 2016

Czechoslovak Mathematical Journal

Let 𝐌 m , n be the set of all m × n real matrices. A matrix A 𝐌 m , n is said to be row-dense if there are no zeros between two nonzero entries for every row of this matrix. We find the structure of linear functions T : 𝐌 m , n 𝐌 m , n that preserve or strongly preserve row-dense matrices, i.e., T ( A ) is row-dense whenever A is row-dense or T ( A ) is row-dense if and only if A is row-dense, respectively. Similarly, a matrix A 𝐌 n , m is called a column-dense matrix if every column of A is a column-dense vector. At the end, the structure of linear...

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