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Exchange matrix

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Square matrix whose entries are 1 along the anti-diagonal and 0 elsewhere

In mathematics, especially linear algebra, the exchange matrices (also called the reversal matrix, backward identity, or standard involutory permutation) are special cases of permutation matrices, where the 1 elements reside on the antidiagonal and all other elements are zero. In other words, they are 'row-reversed' or 'column-reversed' versions of the identity matrix.

J 2 = ( 0 1 1 0 ) J 3 = ( 0 0 1 0 1 0 1 0 0 ) J n = ( 0 0 0 1 0 0 1 0 j ˙ 0 1 0 0 1 0 0 0 ) {\displaystyle {\begin{aligned}J_{2}&={\begin{pmatrix}0&1\\1&0\end{pmatrix}}\\J_{3}&={\begin{pmatrix}0&0&1\\0&1&0\\1&0&0\end{pmatrix}}\\&\quad \vdots \\J_{n}&={\begin{pmatrix}0&0&\cdots &0&1\\0&0&\cdots &1&0\\\vdots &\vdots &\,{}_{_{\displaystyle \cdot }}\!\,{}^{_{_{\displaystyle \cdot }}}\!{\dot {\phantom {j}}}&\vdots &\vdots \\0&1&\cdots &0&0\\1&0&\cdots &0&0\end{pmatrix}}\end{aligned}}}

Definition

If J is an n × n exchange matrix, then the elements of J are J i , j = { 1 , i + j = n + 1 0 , i + j n + 1 {\displaystyle J_{i,j}={\begin{cases}1,&i+j=n+1\\0,&i+j\neq n+1\\\end{cases}}}

Properties

  • Premultiplying a matrix by an exchange matrix flips vertically the positions of the former's rows, i.e., ( 0 0 1 0 1 0 1 0 0 ) ( 1 2 3 4 5 6 7 8 9 ) = ( 7 8 9 4 5 6 1 2 3 ) . {\displaystyle {\begin{pmatrix}0&0&1\\0&1&0\\1&0&0\end{pmatrix}}{\begin{pmatrix}1&2&3\\4&5&6\\7&8&9\end{pmatrix}}={\begin{pmatrix}7&8&9\\4&5&6\\1&2&3\end{pmatrix}}.}
  • Postmultiplying a matrix by an exchange matrix flips horizontally the positions of the former's columns, i.e., ( 1 2 3 4 5 6 7 8 9 ) ( 0 0 1 0 1 0 1 0 0 ) = ( 3 2 1 6 5 4 9 8 7 ) . {\displaystyle {\begin{pmatrix}1&2&3\\4&5&6\\7&8&9\end{pmatrix}}{\begin{pmatrix}0&0&1\\0&1&0\\1&0&0\end{pmatrix}}={\begin{pmatrix}3&2&1\\6&5&4\\9&8&7\end{pmatrix}}.}
  • Exchange matrices are symmetric; that is: J n T = J n . {\displaystyle J_{n}^{\mathsf {T}}=J_{n}.}
  • For any integer k: J n k = { I  if  k  is even, J n  if  k  is odd. {\displaystyle J_{n}^{k}={\begin{cases}I&{\text{ if }}k{\text{ is even,}}\\J_{n}&{\text{ if }}k{\text{ is odd.}}\end{cases}}} In particular, Jn is an involutory matrix; that is, J n 1 = J n . {\displaystyle J_{n}^{-1}=J_{n}.}
  • The trace of Jn is 1 if n is odd and 0 if n is even. In other words: tr ( J n ) = 1 ( 1 ) n 2 = n mod 2 . {\displaystyle \operatorname {tr} (J_{n})={\frac {1-(-1)^{n}}{2}}=n{\bmod {2}}.}
  • The determinant of Jn is: det ( J n ) = ( 1 ) n / 2 = ( 1 ) n ( n 1 ) 2 {\displaystyle \det(J_{n})=(-1)^{\lfloor n/2\rfloor }=(-1)^{\frac {n(n-1)}{2}}} As a function of n, it has period 4, giving 1, 1, −1, −1 when n is congruent modulo 4 to 0, 1, 2, and 3 respectively.
  • The characteristic polynomial of Jn is: det ( λ I J n ) = ( λ 1 ) n / 2 ( λ 1 ) n / 2 = { [ ( λ + 1 ) ( λ 1 ) ] n 2  if  n  is even, ( λ 1 ) n + 1 2 ( λ + 1 ) n 1 2  if  n  is odd, {\displaystyle \det(\lambda I-J_{n})=(\lambda -1)^{\lceil n/2\rceil }(\lambda -1)^{\lfloor n/2\rfloor }={\begin{cases}{\big }^{\frac {n}{2}}&{\text{ if }}n{\text{ is even,}}\\(\lambda -1)^{\frac {n+1}{2}}(\lambda +1)^{\frac {n-1}{2}}&{\text{ if }}n{\text{ is odd,}}\end{cases}}}

its eigenvalues are 1 (with multiplicity n / 2 {\displaystyle \lceil n/2\rceil } ) and -1 (with multiplicity n / 2 {\displaystyle \lfloor n/2\rfloor } ).

  • The adjugate matrix of Jn is: adj ( J n ) = sgn ( π n ) J n . {\displaystyle \operatorname {adj} (J_{n})=\operatorname {sgn}(\pi _{n})J_{n}.} (where sgn is the sign of the permutation πk of k elements).

Relationships

  • An exchange matrix is the simplest anti-diagonal matrix.
  • Any matrix A satisfying the condition AJ = JA is said to be centrosymmetric.
  • Any matrix A satisfying the condition AJ = JA is said to be persymmetric.
  • Symmetric matrices A that satisfy the condition AJ = JA are called bisymmetric matrices. Bisymmetric matrices are both centrosymmetric and persymmetric.

See also

  • Pauli matrices (the first Pauli matrix is a 2 × 2 exchange matrix)

References

  1. Horn, Roger A.; Johnson, Charles R. (2012), "§0.9.5.1 n-by-n reversal matrix", Matrix Analysis (2nd ed.), Cambridge University Press, p. 33, ISBN 978-1-139-78888-5.
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