Column Rank of Matrix equals Row Rank/Proof using Orthogonality

Theorem

Let $\mathbf A$ be a matrix.

The column rank of $\mathbf A$ is equal to the row rank of $\mathbf A$.

Proof

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Let $\map c {\mathbf A}$ denote the column rank of $\mathbf A$.

Let $\map r {\mathbf A}$ denote the row rank of $\mathbf A$.

Let $\mathbf A$ be an $m\times n$ matrix whose row rank is $r$.

Therefore, the dimension of the row space of $\mathbf A$ is $r$.

Let $\mathbf x_1, \ldots, \mathbf x_r$ be a basis of the row space of $\mathbf A$.

We claim that the vectors $\mathbf A \mathbf x_1, \ldots, \mathbf A \mathbf x_r$ are linearly independent.

To see why, consider the following linear combination of these vectors with scalar coefficients $c_1, \ldots, c_r$:

$\mathbf 0 = c_1 \mathbf A \mathbf x_1 + c_2 \mathbf A \mathbf x_2 + \cdots + c_r \mathbf A \mathbf x_r = \mathbf A \paren {c_1 \mathbf x_1 + c_2 \mathbf x_2 + \cdots + c_r \mathbf x_r} = \mathbf A \mathbf v$

where $\mathbf v = c_1 \mathbf x_1 + \cdots + c_r \mathbf x_r$.

We make two observations:

$(\text a) \mathbf v$ is a linear combination of vectors in the row space of $\mathbf A$, which implies that $\mathbf v$ belongs to the row space of $\mathbf A$

$(\text b)$ since $\mathbf A \mathbf v = \mathbf 0$, the vector $\mathbf v$ is orthogonal to every row vector of $\mathbf A$ and, hence, is orthogonal to every vector in the row space of $\mathbf A$.

The facts $(\text a)$ and $(\text b)$ together imply that $\mathbf v$ is orthogonal to itself, which proves that $\mathbf v = \mathbf 0$.

Therefore:

$c_1 \mathbf x_1 + c_2 \mathbf x_2 + \cdots + c_r \mathbf x_r = \mathbf 0$

But recall that $\mathbf x_1, \ldots, \mathbf x_r$ were chosen as a basis of the row space of $\mathbf A$ and so are linearly independent.

This implies that

$c_1 = \cdots = c_r = 0$

It follows that $\mathbf A \mathbf x_1,\ldots, \mathbf A \mathbf x_r$ are linearly independent.

Now, $\mathbf A \mathbf x_1, \ldots, \mathbf A \mathbf x_r$ are vectors in the column space of $\mathbf A$.

From the above argument, they are also $r$ linearly independent vectors in the column space of $\mathbf A$.

Therefore, the dimension of the column space of $\mathbf A$ (that is, the column rank of $\mathbf A$) must be at least as big as $r$.

This proves that $\map r {\mathbf A} \le \map c {\mathbf A}$, that is, row rank of $\mathbf A$ is no larger than the column rank of $\mathbf A$.

This result applies to any matrix $\mathbf{A}$.

So, we can apply this result to the transpose of $\mathbf A$ to get the reverse inequality:

$\map r {\mathbf A^\intercal} \le \map c {\mathbf A^\intercal}$

Combining these inequalities, we conclude that row rank equals column rank as follows:

$\map r {\mathbf A} \le \map c {\mathbf A} = \map r {\mathbf A^\intercal} \le \map c {\mathbf A^\intercal} = \map r {\mathbf A}$

$\blacksquare$

Sources

• 1995: George Mackiw: A Note on the Equality of the Column and Row Rank of a Matrix (Math. Mag. Vol. 68, no. 4: pp. 285 – 286)