# Singular Values

matrix

In a previous post, we have seen the effect of multiplying a matrix with its eigenvectors. The vector does not change in direction, merely shrinks/stretches by an amount proportional to the corresponding eigenvalue.

I reproduce the before and after plots below for three matrices $\mathbf{A}$, $\mathbf{B}$, and $\mathbf{C}$.

There is one subtle difference between $\mathbf{B}$, $\mathbf{C}$, and $\mathbf{A}$. Take $\mathbf{B}$ for example, the length of $\mathbf{B}\mathbf{u}_1$ is the maximum of $\|\mathbf{B}\mathbf{x}\|$ over all unit vectors $\mathbf{x}$. And the length of $\mathbf{B}\mathbf{u}_2$ is the maximum of $\|\mathbf{B}\mathbf{x}\|$ over all unit vectors $\mathbf{x}$ that are perpendicular to $\mathbf{u}_1$. The same pattern applies to $\mathbf{C}$ as well. However, $\mathbf{A}\mathbf{u}_1$ is certainly NOT the maximum of $\|\mathbf{A}\mathbf{x}\|$ over all unit vectors $\mathbf{x}$.

As always, there is no coincidence in mathematics. Nor is this one. For a symmetric matrix $\mathbf{M}$, $\mathbf{M}\mathbf{u}_i$ returns the maximum of $\|\mathbf{M}\mathbf{x}\|$ over all unit vectors $\mathbf{x}$ that are perpendicular to the first $i - 1$ eigenvectors of $\mathbf{M}$. The question remains then: among all unit vectors $\mathbf{x}$, which one maximizes $\|\mathbf{A}\mathbf{x}\|$ when $\mathbf{A}$ is not necessarily symmetric?

Let’s digress here for a moment and consider, not $\mathbf{A}$, but $\mathbf{A}^\top\mathbf{A}$. Given that the transpose of a product is the product of the transpose in the reverse order, we have

$$ \begin{equation*} (\mathbf{A}^\top\mathbf{A})^\top = \mathbf{A}^\top(\mathbf{A}^\top)^\top = \mathbf{A}^\top\mathbf{A} \end{equation*} $$

In other words, $\mathbf{A}^\top\mathbf{A}$ is equal to its transpose, and therefore is a symmetric matrix. From previous posts, we know that a symmetric matrix such as $\mathbf{A}^\top\mathbf{A}$ has $n$ real eigenvalues and $n$ linearly independent and orthogonal eigenvectors.

Next, let’s calculate the eigenvalues and eigenvectors of $\mathbf{A}^\top\mathbf{A}$.

Let’s label these eigenvectors as $\mathbf{v}_1$ and $\mathbf{v}_2$, and we can assume that they are normalized.

Before we proceed, take a guess at what you would see if we plot $\mathbf{v}_1$, $\mathbf{v}_2$, $\mathbf{A}\mathbf{v}_1$ and $\mathbf{A}\mathbf{v}_2$.

Recall the question we asked earlier: Among all unit vectors $\mathbf{x}$, which one maximizes $\|\mathbf{A}\mathbf{x}\|$? It seems that we have found the answer. It is the eigenvectors of $\mathbf{A}^\top\mathbf{A}$.

We have shown that this is true in the example of matrix $\mathbf{A}$. In general, for an $m \times n$ matrix $\mathbf{A}$, it can be shown that $\mathbf{A}\mathbf{v}_i$ has the greatest length and is perpendicular to the pervious $i - 1$ eigenvectors, where $\mathbf{v}_1, \mathbf{v}_2, \ldots, \mathbf{v}_n$ are eigenvectors of $\mathbf{A}^T\mathbf{A}$.

For each of these eigenvectors, we can use the definition of length and the rule for the product of transposed matrices to have:

$$ \begin{equation*} \|\mathbf{A}\mathbf{v}_i\|^2 = (\mathbf{A}\mathbf{v}_i)^T\mathbf{A}\mathbf{v}_i = \mathbf{v}_i^T\mathbf{A}^T\mathbf{A}\mathbf{v}_i \end{equation*} $$

Let’s assume that the corresponding eigenvalue of $\mathbf{v}_i$ is $\lambda_i$

$$ \mathbf{v}_i^T\mathbf{A}^T\mathbf{A}\mathbf{v}_i = \mathbf{v}_i^T\lambda_i\mathbf{v}_i = \lambda_i\mathbf{v}_i^T\mathbf{v}_i $$

And because $\mathbf{v}_i$ is normalized, so

$$ \|\mathbf{v}_i\|^2 = \mathbf{v}_i^T\mathbf{v}_i = 1 $$

and

$$ \|\mathbf{A}\mathbf{v}_i\|^2 = \lambda_i\mathbf{v}_i^T\mathbf{v}_i = \lambda_i $$

This result shows that all the eigenvalues of $\mathbf{A}^\top\mathbf{A}$ are non-negative. If we label them in descending order, we have:

$$ \lambda_1 \geq \lambda_2 \geq \cdots \geq \lambda_n \geq 0 $$

**The singular value of $\mathbf{A}$ is defined as the square root of $\lambda_i$,
denoted $\sigma_i$.**

$$ \sigma_i = \sqrt{\lambda_i} = \|\mathbf{A}\mathbf{v}_i\|,~ \sigma_1 \geq \sigma_2 \geq \cdots \geq \sigma_n \geq 0 $$

Therefore, the singular values of $\mathbf{A}$ are the length of vectors $\mathbf{A}\mathbf{v}_i$. An important theory that forms the backbone of the SVD method: the maximum value of $\|\mathbf{A}\mathbf{x}\|$, subject to the constraints

$$ \|\mathbf{x}\| = 1,~ \mathbf{x}^\top\mathbf{v}_1 = 0,~ \mathbf{x}^\top\mathbf{v}_2 = 0,~ \ldots,~ \mathbf{x}^\top \mathbf{v}_{k-1} = 0 $$

is $\sigma_k$, and this maximum value is attained at $\mathbf{v}_k$, the $k$-th eigenvector of $\mathbf{A}^T\mathbf{A}$.

In an earlier post, we mentioned that a symmetric matrix transforms a vector by stretching or shrinking the vector along the eigenvectors of this matrix.

With a non-symmetric matrix $\mathbf{A}$, it transforms a vector by stretching or shrinking the vector along the direction of $\mathbf{A}\mathbf{v}_i$, where $\mathbf{v}_i$ is an eigenvector of $\mathbf{A}^T\mathbf{A}$, ordered based on its corresponding eigenvalue, $\|\mathbf{v}_i\| = 1$. The corresponding singular value $\sigma_i$ is the scalar that determines the length of the stretching, $\sigma_i = \sqrt{\lambda_i}$, where $\lambda_i$ is the corresponding eigenvalue of $\mathbf{A}^\top\mathbf{A}$.

How can we reconcile these two seemingly different rules? Let’s take a symmetric metrix, $\mathbf{B}$. Suppose that its $i$-th eigenvector is $\mathbf{u}_i$ and the corresponding eigenvalue is $\lambda_i$. If we multiply $\mathbf{B}^\top\mathbf{B}$ by $\mathbf{u}_i$ we get:

$$ \begin{equation*} \left(\mathbf{B}^\top\mathbf{B}\right)\mathbf{u}_i = \mathbf{B}\left(\mathbf{B}\mathbf{u}_i\right) = \mathbf{B}\lambda_i\mathbf{u}_i = \lambda_i\mathbf{B}\mathbf{u}_i = \lambda_i^2\mathbf{u}_i \end{equation*} $$

which means that $\mathbf{u}_i$ is also an eigenvector of $\mathbf{B}^\top\mathbf{B}$, but its corresponding eigenvalue is $\lambda_i^2$! Now we can see that the previous rule about a symmetric matrix is nothing but a special case of the more general rule:

A matrix $\mathbf{A}$ transforms a vector by stretching or shrinking the vector along the direction of $\mathbf{A}\mathbf{v}_i$, where $\mathbf{v}_i$ is an eigenvector of $\mathbf{A}^T\mathbf{A}$, ordered based on its corresponding singular value. The corresponding singular value $\sigma_i$ is the scalar that determines the length of the stretching or shrinking, $\sigma_i = \sqrt{\lambda_i}$, where $\lambda_i$ is the corresponding eigenvalue of $\mathbf{A}^\top\mathbf{A}$.

When $\mathbf{A}$ is symmetric, the direction of $\mathbf{A}\mathbf{v}_i$ will be identical to that of $\mathbf{A}\mathbf{u}_i$, because $\mathbf{A}$ has the same eigenvectors as $\mathbf{A}^\top\mathbf{A}$. Moreover, $\mathbf{A}\mathbf{u}_i = \lambda_i\mathbf{u}_i$. Therefore, the direction of $\mathbf{A}\mathbf{v}_i$ is the direction of $\mathbf{A}\mathbf{u}_i$, which is the direction of $\mathbf{u}_i$. That is, a symmetric matrix transforms a vector by stretching or shrinking the vector along the direction of $\mathbf{u}_i$, its eigenvector!

What about the length of the stretching or shrinking? We know that $\lambda_i = \sqrt{\lambda_i^2}$, where $\lambda_i^2$ is the corresponding eigenvalue of $\mathbf{A}^\top\mathbf{A}$, and $\lambda_i$ is the corresponding eigenvalue of $\mathbf{A}$. Therefore, a symmetric matrix transforms a vector along its eigenvectors $\mathbf{u}_i$, scaled by its corresponding eigenvalues $\lambda_i$. We have come a full circle! In the next post, we are finally ready to present the singular value decomposition equation!