The SVD theorem

L. El Ghaoui

The SVD theorem

The SVD theorem
Geometry
Link with the spectral theorem

The SVD theorem

Basic idea

Recall from here that any matrix $A \in \mathbb{R}^{m \times n}$ with rank one can be written as

$A = \sigma u v^{T},$

where $u \in \mathbb{R}^{m}, v \in \mathbb{R}^{n}$ , and $\sigma > 0$ .

It turns out that a similar result holds for matrices of arbitrary rank $r$ . That is, we can express any matrix $A \in \mathbb{R}^{m \times n}$ with rank one as sum of rank-one matrices

$A = \sum_{i=1}^{n}\sigma_{i} u_{i} v_{i}^{T},$

where $u_{1},\dots, u_{r}$ are mutually orthogonal, $v_{1},\dots,v_{r}$ are also mutually orthogonal, and the $\sigma_{i}$ ‘s are positive numbers called the singular values of $A$ . In the above, $r$ turns out to be the rank of $A$ .

Theorem statement

The following important result applies to any matrix $A$ , and allows to understand the structure of the mapping $x \rightarrow Ax$ .

Theorem: Singular Value Decomposition (SVD)

An arbitrary matrix $A \in \mathbb{R}^{m \times n}$ admits a decomposition of the form

$A = \sum_{i=1}^{n}\sigma_{i} u_{i} v_{i}^{T} = U\tilde{S}V^{T}, \tilde{S} := \begin{pmatrix} S & 0\\ 0 & 0 \end{pmatrix},$

where $U \in \mathbb{R}^{m \times m}, V \in \mathbb{R}^{n \times n}$ are both orthogonal matrices, and the matrix $S$ is diagonal:

$S = diag(\sigma_{1},\dots,\sigma_{r}),$

where the positive numbers $\sigma_{1} \ge \dots \ge \sigma_{r} > 0$ are unique, and are called the singular values of $A$ . The number $r \le \min(m, n)$ is equal to the rank of $A$ , and the triplet $(U, \tilde{S}, V)$ is called a singular value decomposition (SVD) of $A$ . The first $r$ columns of $U$ : $u_{i}, i=1,\dots,r$ (resp. $V$ : $v_{i}, i=1,\dots,r$ ) are called left (resp. right) singular vectors of $A$ , and satisfy

$Av_{i}=\sigma_{i}u_{i}, u_{i}^{T}A=\sigma_{i}v_{i}, i =1, \dots, r.$

The proof of the theorem hinges on the spectral theorem for symmetric matrices. Note that in the theorem, the zeros appearing alongside $S$ are really blocks of zeros. They may be empty, for example if $r=n$ , then there are no zeros to the right of $S$ .

Computing the SVD

The SVD of a $m \times n$ matrix $A$ can be easily computed via a sequence of linear transformations. The complexity of the algorithm, expressed roughly as the number of floating point operations per seconds it requires, grows as

$O(nm\min(n,m))$

. This can be substantial for large, dense matrices. For sparse matrices, we can speed up the computation if we are interested only in the largest few singular values and associated singular vectors.

Matlab syntax

>> [U,Stilde,V] = svd(A); % this produces the SVD of A, with Stilde of same size as A
>> [Uk,Sk,Vk] = svds(A,k); % the k largest singular values of A, assuming A is sparse

Example: A $4 \times 5$ example.

Geometry

The theorem allows to decompose the action of $A$ on a given input vector as a three-step process. To get $Ax$ , where $x \in \mathbb{R}^{n}$ , we first form $\tilde{x} := V^{T}x \in \mathbb{R}^{n}$ . Since $V$ is an orthogonal matrix, $V^{T}$ is also orthogonal, and $\tilde{x}$ is just a rotated version of $x$ , which still lies in the input space. Then we act on the rotated vector $\tilde{x}$ by scaling its elements. Precisely, the first $r$ elements of $\tilde{x}$ are scaled by the singular values $\sigma_{1},\dots,\sigma_{r}$ ; the remaining $n-r$ elements are set to zero. This step results in a new vector $\tilde{y}$ which now belongs to the output space $\mathbb{R}^{m}$ . The final step consists in rotating the vector $\tilde{y}$ by the orthogonal matrix $U$ , which results in $y= U\tilde{y}=Ax$ .

Example: Assume $A$ has the simple form

$A = \begin{pmatrix} 1.3 7 0 \\ 0 & 2.1 \\ 0 & 0 \end{pmatrix},$

then for an input vector $x$ in $\mathbb{R}^{2}$ , $Ax$ is a vector in $\mathbb{R}^{3}$ with first component $1.3x_{1}$ ,, second component $2.1x_{2}$ , and last component zero.

To summarize, the SVD theorem states that any matrix-vector multiplication can be decomposed as a sequence of three elementary transformations: a rotation in the input space, a scaling that goes from the input space to the output space, and a rotation in the output space. In contrast with symmetric matrices, input and output directions are different.

The interpretation allows to make a few statements about the matrix.

Example: A $4 \times 4$ example.

Link with the SED

If $A$ admits an SVD, then the matrices $AA^{T}$ and $A^{T}A$ has the following SEDs:

$A A^T=U \Lambda_m U^T, \quad A^T A=V \Lambda_n V^T$

where

$\Lambda_m := \tilde{S}\tilde{S}^{T} = diag(\sigma_{1},\dots,\sigma_{r},0,\dots,0)$

is $m \times m$ (so it has $m-r$ trailing zeros), and

$\Lambda_m := \tilde{S}^{T}\tilde{S} = diag(\sigma_{1},\dots,\sigma_{r},0,\dots,0)$

is $n \times n$ (so it has $n-r$ trailing zeros). The eigenvalues of $AA^{T}$ and $A^{T}A$ are the same, and equal to the squared singular values of $A$ . The corresponding eigenvectors are the left and right singular vectors of $A$ .

This is a method (not the most computationally efficient) to find the SVD of a matrix, based on the SED.

The SVD theorem

The SVD theorem

Basic idea

Theorem statement

Computing the SVD

Geometry

Link with the SED

License

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