In mathematics, a jacket matrix is a square symmetric matrix A = ( a i j ) {\displaystyle A=(a_{ij})} of order n if its entries are non-zero and real, complex, or from a finite field, and
A B = B A = I n {\displaystyle \ AB=BA=I_{n}}
where In is the identity matrix, and
B = 1 n ( a i j − 1 ) T . {\displaystyle \ B={1 \over n}(a_{ij}^{-1})^{T}.}
where T denotes the transpose of the matrix. In other words, the inverse of a jacket matrix is determined by its element-wise or block-wise inverse. The definition above may also be expressed as:
∀ u , v ∈ { 1 , 2 , … , n } : a i u , a i v ≠ 0 , ∑ i = 1 n a i u − 1 a i v = { n , u = v 0 , u ≠ v {\displaystyle \forall u,v\in \{1,2,\dots ,n\}:~a_{iu},a_{iv}\neq 0,~~~~\sum _{i=1}^{n}a_{iu}^{-1}\,a_{iv}={\begin{cases}n,&u=v\\0,&u\neq v\end{cases}}}
The jacket matrix is a generalization of the Hadamard matrix; it is a diagonal block-wise inverse matrix.
Motivation
As shown in the table, i.e. in the series, for example with n=2, forward: 2 2 = 4 {\displaystyle 2^{2}=4} , inverse : ( 2 2 ) − 1 = 1 4 {\displaystyle (2^{2})^{-1}={1 \over 4}} , then, 4 ∗ 1 4 = 1 {\displaystyle 4*{1 \over 4}=1} . That is, there exists an element-wise inverse.
Example 1.
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