# Shift matrix

In mathematics, a shift matrix is a binary matrix with ones only on the superdiagonal or subdiagonal, and zeroes elsewhere. A shift matrix U with ones on the superdiagonal is an upper shift matrix. The alternative subdiagonal matrix L is unsurprisingly known as a lower shift matrix. The (i, j )th component of U and L are
    $U_{ij}=\delta _{i+1,j},\quad L_{ij}=\delta _{i,j+1},$
where $\delta _{ij}$ is the Kronecker delta symbol.
For example, the 5 × 5 shift matrices are
    $U_{5}={\begin{pmatrix}0&1&0&0&0\\0&0&1&0&0\\0&0&0&1&0\\0&0&0&0&1\\0&0&0&0&0\end{pmatrix}}\quad L_{5}={\begin{pmatrix}0&0&0&0&0\\1&0&0&0&0\\0&1&0&0&0\\0&0&1&0&0\\0&0&0&1&0\end{pmatrix}}.$
Clearly, the transpose of a lower shift matrix is an upper shift matrix and vice versa.
As a linear transformation, a lower shift matrix shifts the components of a column vector one position down, with a zero appearing in the first position. An upper shift matrix shifts the components of a column vector one position up, with a zero appearing in the last position.
Premultiplying a matrix A by a lower shift matrix results in the elements of A being shifted downward by one position, with zeroes appearing in the top row. Postmultiplication by a lower shift matrix results in a shift left. Similar operations involving an upper shift matrix result in the opposite shift.
Clearly all finite-dimensional shift matrices are nilpotent; an n × n shift matrix S becomes the zero matrix when raised to the power of its dimension n.
Shift matrices act on shift spaces. The infinite-dimensional shift matrices are particularly important for the study of ergodic systems. Important examples of infinite-dimensional shifts are the Bernoulli shift, which acts as a shift on Cantor space, and the Gauss map, which acts as a shift on the space of continued fractions (that is, on Baire space.)

## Properties

Let L and U be the n × n lower and upper shift matrices, respectively. The following properties hold for both U and L. Let us therefore only list the properties for U:
* det(U) = 0
* tr(U) = 0
* rank(U) = n − 1
* The characteristic polynomials of U is
    $p_{U}(\lambda )=(-1)^{n}\lambda ^{n}.$
* U$^n$ = 0. This follows from the previous property by the Cayley–Hamilton theorem.
* The permanent of U is 0.
The following properties show how U and L are related:
* L$^T$ = U; U$^T$ = L
* The null spaces of U and L are
    $N(U)=\operatorname {span} \left\{(1,0,\ldots ,0)^{\mathsf {T}}\right\},$
    $N(L)=\operatorname {span} \left\{(0,\ldots ,0,1)^{\mathsf {T}}\right\}.$
* The spectrum of U and L is $\{0\}$. The algebraic multiplicity of 0 is n, and its geometric multiplicity is 1. From the expressions for the null spaces, it follows that (up to a scaling) the only eigenvector for U is $(1,0,\ldots ,0)^{\mathsf {T}}$, and the only eigenvector for L is $(0,\ldots ,0,1)^{\mathsf {T}}$.
* For LU and UL we have These matrices are both idempotent, symmetric, and have the same rank as U and L
    $UL=I-\operatorname {diag} (0,\ldots ,0,1),$
    $LU=I-\operatorname {diag} (1,0,\ldots ,0).$
* L$^{n−a}$U$^{n−a}$ + L$^a$U$^a$ = U$^{n−a}$L$^{n−a}$ + U$^a$L$^a$ = I (the identity matrix), for any integer a between 0 and n inclusive.
If N is any nilpotent matrix, then N is similar to a block diagonal matrix of the form
    ${\begin{pmatrix}S_{1}&0&\ldots &0\\0&S_{2}&\ldots &0\\\vdots &\vdots &\ddots &\vdots \\0&0&\ldots &S_{r}\end{pmatrix}}$
where each of the blocks S$_1$, S$_2$, ..., S$_r$ is a shift matrix (possibly of different sizes).

## Examples

    $S={\begin{pmatrix}0&0&0&0&0\\1&0&0&0&0\\0&1&0&0&0\\0&0&1&0&0\\0&0&0&1&0\end{pmatrix}};\quad A={\begin{pmatrix}1&1&1&1&1\\1&2&2&2&1\\1&2&3&2&1\\1&2&2&2&1\\1&1&1&1&1\end{pmatrix}}.$
Then,
    $SA={\begin{pmatrix}0&0&0&0&0\\1&1&1&1&1\\1&2&2&2&1\\1&2&3&2&1\\1&2&2&2&1\end{pmatrix}};\quad AS={\begin{pmatrix}1&1&1&1&0\\2&2&2&1&0\\2&3&2&1&0\\2&2&2&1&0\\1&1&1&1&0\end{pmatrix}}.$
Clearly there are many possible permutations. For example, $S^{\mathsf {T}}AS$ is equal to the matrix A shifted up and left along the main diagonal.
    $S^{\mathsf {T}}AS={\begin{pmatrix}2&2&2&1&0\\2&3&2&1&0\\2&2&2&1&0\\1&1&1&1&0\\0&0&0&0&0\end{pmatrix}}.$