Rank-one matrices: A representation theorem

We prove the theorem mentioned here:

Theorem: outer product representation of a rank-one matrix

 

Every rank-one matrix A \in \mathbb{R}^{m \times n} can be written as an ‘‘outer product’’, or dyad:

    \[A = pq^T,\]

where p \in \mathbb{R}^{m}, \; q \in \mathbb{R}^{n}.

Proof: For any non-zero vectors p \in \mathbb{R}^{m}, q \in \mathbb{R}^{n}, the matrix pq^T is indeed of rank one: if x \in \mathbb{R}^{n}, then

    \[pq^Tx=(q^Tx)p.\]

When x spans \mathbb{R}^{n}, the scalar q^Tx spans the entire real line (since q \neq 0), and the vector (q^Tx)p spans the subspace of vectors proportional to p. Hence, the range of pq^T is the line:

    \[\mathbf{R}(pq^T) = \{tp\; : \;t \in \mathbb{R}\},\]

which is of dimension 1.

Conversely, if A is of rank one, then its range is of dimension one, hence it must be a line passing through 0. Hence for any x there exist a function t: \mathbb{R} \rightarrow \mathbb{R} such that

    \[Ax = t(x)p.\]

Using x = e_i, where e_i is the i-th vector of the standard basis, we obtain that there exist numbers t_1, ... t_n such that for every i:

    \[Ae_i = t_ip, \quad i=1,...,n.\]

We can write the above in a single matrix equation:

    \[ A \begin{pmatrix} e_1 & \cdots & e_n \end{pmatrix} = p \begin{pmatrix} t_1 & \cdots & t_n \end{pmatrix} \]

Now letting q = \begin{pmatrix} t_1 \\ \vdots \\ t_n \end{pmatrix} \in \mathbb{R}^{n} and realizing that the matrix (e_1, ..., e_n) is simply the identity matrix, we obtain A = pq^T, as desired.

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