What Is a Nilpotent Matrix?

An n\times n matrix A is nilpotent if A^k =0 for some positive integer k. A nonzero nilpotent matrix must have both positive and negative entries in order for cancellation to take place in the matrix powers. The smallest k for which A^k =0 is called the index of nilpotency. The index does not exceed n, as we will see below.

Here are some examples of nilpotent matrices.

\notag \begin{aligned}  A_1 &= \begin{bmatrix}0 & 1 \\ 0 & 0 \end{bmatrix}, \quad         A_1^2=0,\\  A_2 &= \begin{bmatrix}0 & 1 & 1\\ 0 & 0 & 1\\ 0 & 0 & 0 \end{bmatrix}, \quad         A_2^3 = 0,\\  A_3 &= \begin{bmatrix}1 & -1 \\ 1 & -1 \end{bmatrix}, \quad         A_3^2 = 0,\\  A_4 &= \begin{bmatrix}2 & 2 & 4\\ -4 & -4 & -8\\ 1 & 1 & 2  \end{bmatrix}, \quad         A_4^2 = 0. \end{aligned}

Matrix A_1 is the 2\times 2 instance of the upper bidiagonal p\times p matrix

\notag N =  \begin{bmatrix}  0   & 1         &          &           \\                           & 0         & \ddots   &           \\                           &           & \ddots   &    1      \\                           &           &          & 0         \end{bmatrix},   \qquad (1)

for which

\notag N^2     = \begin{bmatrix}                0 & 0 & 1      &        &   \\                  & 0 & \ddots & \ddots &   \\                  &   &        & \ddots & 1 \\                  &   &        & \ddots & 0 \\                  &   &        &        & 0            \end{bmatrix}, \quad \dots, \quad N^{p-1} = \begin{bmatrix}                0 & 0 & \dots  & 0      & 1      \\                  & 0 & \ddots &        & 0      \\                  &   & \ddots & \ddots & \vdots \\                  &   &        & 0      & 0      \\                  &   &        &        & 0            \end{bmatrix}

and N^p = 0. The superdiagonal of ones moves up to the right with each increase in the index of the power until it disappears off the top right corner of the matrix.

Matrix A_4 has rank 1 and was constructed using a general formula: if A = xy^T with y^Tx = 0 then A^2 = xy^T xy^T = (y^Tx) xy^T = 0. We simply took orthogonal vectors x =[2, -4, 1]^T and y = [1, 1, 2]^T.

If A is nilpotent then every eigenvalue is zero, since Ax = \lambda x with x\ne 0 implies 0 = A^nx = \lambda^n x or \lambda = 0. Consequently, the trace and determinant of a nilpotent matrix are both zero.

If A is nilpotent and Hermitian or symmetric, or more generally normal (A^*A = AA^*), then A = 0, since such a matrix has a spectral decomposition A = Q \mathrm{diag}(\lambda_i)Q^* and the matrix \mathrm{diag}(\lambda_i) is zero. It is only for nonnormal matrices that nilpotency is a nontrivial property, and the best way to understand it is with the Jordan canonical form (JCF). The JCF of a matrix with only zero eigenvalues has the form A = XJX^{-1}, where J = \mathrm{diag}(J_{m_1},J_{m_2}, \dots, J_{m_p}), where J_{m_i} is of the form (1) and hence J_{m_i}^{m_i} = 0. It follows that the index of nilpotency is k = \max\{\,m_i : i=1\colon p\,\} \le n.

What is the rank of an n\times n nilpotent matrix A? The minimum possible rank is 0, attained for the zero matrix. The maximum possible rank is n-1, attained when the JCF of A has just one Jordan block of size n. Any rank between 0 and n-1 is possible: rank j is attained when there is a Jordan block of size j+1 and all other blocks are 1\times 1.

Finally, while a nilpotent matrix is obviously not invertible, like every matrix it has a Moore–Penrose pseudoinverse. The pseudoinverse of a Jordan block with eigenvalue zero is just the transpose of the block: N^+ = N^T for N in (1).

Related Blog Posts

This article is part of the “What Is” series, available from https://nhigham.com/category/what-is and in PDF form from the GitHub repository https://github.com/higham/what-is.

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