Symmetry pattern of a persymmetric (5 × 5) matrix
In mathematics, a persymmetric matrix is a square matrix that is symmetric with respect to its counter-diagonal .
definition
A square matrix over a body is called persymmetric if for its entries


for applies. The entries of a persymmetrical matrix do not change if they are mirrored on the opposite diagonal.

Examples
For example, a real persymmetric matrix of size is


Generally, persymmetric matrices of size have the form


with .

properties
Symmetries
With the permutation matrix defined by


Persymmetric matrices can also be made compact by the condition

characterize. A bisymmetrical matrix is a persymmetrical matrix that is also symmetrical or centrally symmetrical . A Toeplitz matrix is a persymmetrical matrix whose entries on the main diagonal and all secondary diagonals are constant. A cyclic matrix is a persymmetric matrix whose entries are constant on all diagonals and are repeated cyclically .
Sum and product
The sum of two persymmetric matrices and again results in a persymmetric matrix, as are scalar multiples with . Since the zero matrix is trivially persymmetric, the persymmetric matrices form a sub-vector space in the matrix space .




The product of two persymmetric matrices results from


a persymmetric matrix again if and only if the two matrices and commute .

Inverse
For the inverse of a persymmetric matrix, if it exists

-
.
The inverse of a regular persymmetric matrix is therefore again persymmetric.
See also
literature
- Gene Golub, Charles van Loan: Matrix Computations . JHU Press, 2013, ISBN 978-1-4214-0794-4 .
- Martin Hanke-Bourgeois: Fundamentals of Numerical Mathematics and Scientific Computing . Springer, 2008, ISBN 978-3-8348-0708-3 .
- Roger A. Horn, Charles R. Johnson: Matrix Analysis . Cambridge University Press, 2012, ISBN 978-0-521-83940-2 .
Individual evidence
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↑ Martin Hanke-Bourgeois: Fundamentals of numerical mathematics and scientific computing . Springer, 2008, p. 66 .
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^ Roger A. Horn, Charles Johnson: Matrix analysis . Cambridge University Press, 2013, pp. 36 .
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^ Gene Golub, Charles van Loan: Matrix Computations . JHU Press, 2013, p. 208 .