Singular Value Decompositions

Learning Objectives

Singular Value Decomposition

An \(m \times n\) real matrix \({\bf A}\) has a singular value decomposition of the form

\[{\bf A} = {\bf U} {\bf \Sigma} {\bf V}^T\]

where

\[\begin{eqnarray} {\bf \Sigma} = \begin{bmatrix} \sigma_1 & & \\ & \ddots & \\ & & \sigma_s \\ 0 & & 0 \\ \vdots & \ddots & \vdots \\ 0 & & 0 \end{bmatrix} \text{when } m > n, \; \text{and} \; {\bf \Sigma} = \begin{bmatrix} \sigma_1 & & & 0 & \dots & 0 \\ & \ddots & & & \ddots &\\ & & \sigma_s & 0 & \dots & 0 \\ \end{bmatrix} \text{when} \, m < n. \end{eqnarray}\]

where \(s = \min(m,n)\) and \(\sigma_1 \ge \sigma_2 \dots \ge \sigma_s \ge 0\) are the square roots of the eigenvalues values of \({\bf A}^T {\bf A}\). The diagonal entries are called the singular values of \({\bf A}\).

Time Complexity

The time-complexity for computing the SVD factorization of an arbitrary \(m \times n\) matrix is proportional to \(m^2n + n^3\), where the constant of proportionality ranges from 4 to 10 (or more) depending on the algorithm.

In general, we can define the cost as:

\[\mathcal{O}(m^2n + n^3)\]

Reduced SVD

The SVD factorization of a non-square matrix \({\bf A}\) of size \(m \times n\) can be represented in a reduced format:


The following figure depicts the reduced SVD factorization (in red) against the full SVD factorizations (in gray).

In general, we will represent the reduced SVD as:

\[{\bf A} = {\bf U}_R {\bf \Sigma}_R {\bf V}_R^T\]

where \({\bf U}_R\) is a \(m \times s\) matrix, \({\bf V}_R\) is a \(n \times s\) matrix, \({\bf \Sigma}_R\) is a \(s \times s\) matrix, and \(s = \min(m,n)\).

Example: Computing the SVD

We begin with the following non-square matrix, \({\bf A}\)

\[ {\bf A} = \left[ \begin{array}{ccc} 3 & 2 & 3 \\ 8 & 8 & 2 \\ 8 & 7 & 4 \\ 1 & 8 & 7 \\ 6 & 4 & 7 \\ \end{array} \right] \]

and we will compute the reduced form of the SVD (where here \(s = 3\)):

(1) Compute \({\bf A}^T {\bf A}\):

\[{\bf A}^T {\bf A} = \left[ \begin{array}{ccc} 174 & 158 & 106 \\ 158 & 197 & 134 \\ 106 & 134 & 127 \\ \end{array} \right]\]

(2) Compute the eigenvectors and eigenvalues of \({\bf A}^T {\bf A}\):

\[\lambda_1 = 437.479, \quad \lambda_2 = 42.6444, \quad \lambda_3 = 17.8766, \\ \boldsymbol{v}_1 = \begin{bmatrix} 0.585051 \\ 0.652648 \\ 0.481418\end{bmatrix}, \quad \boldsymbol{v}_2 = \begin{bmatrix} -0.710399 \\ 0.126068 \\ 0.692415 \end{bmatrix}, \quad \boldsymbol{v}_3 = \begin{bmatrix} 0.391212 \\ -0.747098 \\ 0.537398 \end{bmatrix}\]

(3) Construct \({\bf V}_R\) from the eigenvectors of \({\bf A}^T {\bf A}\):

\[ {\bf V}_R = \left[ \begin{array}{ccc} 0.585051 & -0.710399 & 0.391212 \\ 0.652648 & 0.126068 & -0.747098 \\ 0.481418 & 0.692415 & 0.537398 \\ \end{array} \right]. \]

(4) Construct \({\bf \Sigma}_R\) from the square roots of the eigenvalues of \({\bf A}^T {\bf A}\):

\[ {\bf \Sigma}_R = \begin{bmatrix} 20.916 & 0 & 0 \\ 0 & 6.53207 & 0 \\ 0 & 0 & 4.22807 \end{bmatrix} \]

(5) Find \({\bf U}\) by solving \({\bf U}{\bf\Sigma} = {\bf A}{\bf V}\). For our reduced case, we can find \({\bf U}_R = {\bf A}{\bf V}_R {\bf \Sigma}_R^{-1}\). You could also find \({\bf U}\) by computing the eigenvectors of \({\bf AA}^T\).

\[{\bf U} = \overbrace{\left[ \begin{array}{ccc} 3 & 2 & 3 \\ 8 & 8 & 2 \\ 8 & 7 & 4 \\ 1 & 8 & 7 \\ 6 & 4 & 7 \\ \end{array} \right]}^{A} \overbrace{\left[ \begin{array}{ccc} 0.585051 & -0.710399 & 0.391212 \\ 0.652648 & 0.126068 & -0.747098 \\ 0.481418 & 0.692415 & 0.537398 \\ \end{array} \right]}^{V} \overbrace{\left[ \begin{array}{ccc} 0.047810 & 0.0 & 0.0 \\ 0.0 & 0.153133 & 0.0 \\ 0.0 & 0.0 & 0.236515 \\ \end{array} \right]}^{\Sigma^{-1}}\] \[{\bf U} = \left[ \begin{array}{ccc} 0.215371 & 0.030348 & 0.305490 \\ 0.519432 & -0.503779 & -0.419173 \\ 0.534262 & -0.311021 & 0.011730 \\ 0.438715 & 0.787878 & -0.431352\\ 0.453759 & 0.166729 & 0.738082\\ \end{array} \right]\]

We obtain the following singular value decomposition for \({\bf A}\):

\[ \overbrace{\left[ \begin{array}{ccc} 3 & 2 & 3 \\ 8 & 8 & 2 \\ 8 & 7 & 4 \\ 1 & 8 & 7 \\ 6 & 4 & 7 \\ \end{array} \right]}^{A} = \overbrace{\left[ \begin{array}{ccc} 0.215371 & 0.030348 & 0.305490 \\ 0.519432 & -0.503779 & -0.419173 \\ 0.534262 & -0.311021 & 0.011730 \\ 0.438715 & 0.787878 & -0.431352\\ 0.453759 & 0.166729 & 0.738082\\ \end{array} \right]}^{U} \overbrace{\left[ \begin{array}{ccc} 20.916 & 0 & 0 \\ 0 & 6.53207 & 0 \\ 0 & 0 & 4.22807 \\ \end{array} \right]}^{\Sigma} \overbrace{\left[ \begin{array}{ccc} 0.585051 & 0.652648 & 0.481418 \\ -0.710399 & 0.126068 & 0.692415\\ 0.391212 & -0.747098 & 0.537398\\ \end{array} \right]}^{V^T} \]

Recall that we computed the reduced SVD factorization (i.e. \({\bf \Sigma}\) is square, \({\bf U}\) is non-square) here.

Rank, null space and range of a matrix

Suppose \({\bf A}\) is a \(m \times n\) matrix where \(m > n\) (without loss of generality):

\[{\bf A}= {\bf U\Sigma V}^{T} = \begin{bmatrix}\vert & & \vert & & \vert \\ \vert & & \vert & & \vert \\ {\bf u}_1 & \cdots & {\bf u}_n & \cdots & {\bf u}_m\\ \vert & & \vert & & \vert \\\vert & & \vert & & \vert \end{bmatrix} \begin{bmatrix} \sigma_1 & & \\ & \ddots & \\ & & \sigma_n \\ & \vdots & \\ -& 0& -\end{bmatrix} \begin{bmatrix} - & {\bf v}_1^T & - \\ & \vdots & \\ - & {\bf v}_n^T & - \end{bmatrix}\]

We can re-write the above as:

\[{\bf A} = \begin{bmatrix}\vert & & \vert \\ \vert & & \vert \\ {\bf u}_1 & \cdots & {\bf u}_n \\ \vert & & \vert \\ \vert & & \vert \end{bmatrix} \begin{bmatrix} - & \sigma_1 {\bf v}_1^T & - \\ & \vdots & \\ - & \sigma_n{\bf v}_n^T & - \end{bmatrix}\]

Furthermore, the product of two matrices can be written as a sum of outer products:

\[{\bf A} = \sigma_1 {\bf u}_1 {\bf v}_1^T + \sigma_2 {\bf u}_2 {\bf v}_2^T + ... + \sigma_n {\bf u}_n {\bf v}_n^T\]

For a general rectangular matrix, we have:

\[{\bf A} = \sum_{i=1}^{s} \sigma_i {\bf u}_i {\bf v}_i^T\]

where \(s = \min(m,n)\).

If \({\bf A}\) has \(s\) non-zero singular values, the matrix is full rank, i.e. \(\text{rank}({\bf A}) = s\).

If \({\bf A}\) has \(r\) non-zero singular values, and \(r < s\), the matrix is rank deficient, i.e. \(\text{rank}({\bf A}) = r\).

In other words, the rank of \({\bf A}\) equals the number of non-zero singular values which is the same as the number of non-zero diagonal elements in \({\bf \Sigma}\).

Rounding errors may lead to small but non-zero singular values in a rank deficient matrix. Singular values that are smaller than a given tolerance are assumed to be numerically equivalent to zero, defining what is sometimes called the effective rank.

The right-singular vectors (columns of \({\bf V}\)) corresponding to vanishing singular values of \({\bf A}\) span the null space of \({\bf A}\), i.e. null(\({\bf A}\)) = span{\({\bf v}_{r+1}\), \({\bf v}_{r+2}\), …, \({\bf v}_{n}\)}.

The left-singular vectors (columns of \({\bf U}\)) corresponding to the non-zero singular values of \({\bf A}\) span the range of \({\bf A}\), i.e. range(\({\bf A}\)) = span{\({\bf u}_{1}\), \({\bf u}_{2}\), …, \({\bf u}_{r}\)}.

Example:

\[{\bf A} = \left[ \begin{array}{cccc} \frac{1}{\sqrt{2}} & -\frac{1}{\sqrt{2}} & 0 & 0 \\ \frac{1}{\sqrt{2}}2 &\frac{1}{\sqrt{2}} & 0 & 0 \\ 0 & 0 & 0 & 1 \\ 0 & 0 & 1 & 0 \end{array} \right] \left[ \begin{array}{ccc} 14 & 0 & 0 \\ 0 & 14 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{array} \right] \left[ \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right]\]

The rank of \({\bf A}\) is 2.

The vectors \(\left[ \begin{array}{c} \frac{1}{\sqrt{2}} \\ \frac{1}{\sqrt{2}} \\ 0 \\ 0 \end{array} \right]\) and \(\left[ \begin{array}{c} -\frac{1}{\sqrt{2}} \\ \frac{1}{\sqrt{2}} \\ 0 \\ 0 \end{array} \right]\) provide an orthonormal basis for the range of \({\bf A}\).

The vector \(\left[ \begin{array}{c} 0 \\ 0\\ 1 \end{array} \right]\) provides an orthonormal basis for the null space of \({\bf A}\).

(Moore-Penrose) Pseudoinverse

If the matrix \({\bf \Sigma}\) is rank deficient, we cannot get its inverse. We define instead the pseudoinverse:

\[({\bf \Sigma}^+)_{ii} = \begin{cases} \frac{1}{\sigma_i} & \sigma_i \neq 0\\ 0 & \sigma_i = 0 \end{cases}\]

For a general non-square matrix \({\bf A}\) with known SVD (\({\bf A} = {\bf U\Sigma V}^T\)), the pseudoinverse is defined as:

\[{\bf A}^{+} = {\bf V\Sigma}^{+}{\bf U}^T\]

For example, if we consider a \(m \times n\) full rank matrix where \(m > n\):

\[{\bf A}^{+}= \begin{bmatrix} \vert & ... & \vert \\ {\bf v}_1 & ... & {\bf v}_n\\ \vert & ... & \vert \end{bmatrix} \begin{bmatrix} 1/\sigma_1 & & & 0 & \dots & 0 \\ & \ddots & & & \ddots &\\ & & 1/\sigma_n & 0 & \dots & 0 \\ \end{bmatrix} \begin{bmatrix}\vert & & \vert & & \vert \\ \vert & & \vert & & \vert \\ {\bf u}_1 & \cdots & {\bf u}_n & \cdots & {\bf u}_m\\ \vert & & \vert & & \vert \\\vert & & \vert & & \vert \end{bmatrix}^T\]

Euclidean norm of matrices

The induced 2-norm of a matrix \({\bf A}\) can be obtained using the SVD of the matrix :

\[\begin{align} \| {\bf A} \|_2 &= \max_{\|\mathbf{x}\|=1} \|\mathbf{A x}\| = \max_{\|\mathbf{x}\|=1} \|\mathbf{U \Sigma V}^T {\bf x}\| \\ & =\max_{\|\mathbf{x}\|=1} \|\mathbf{ \Sigma V}^T {\bf x}\| = \max_{\|\mathbf{V}^T{\bf x}\|=1} \|\mathbf{ \Sigma V}^T {\bf x}\| =\max_{\|y\|=1} \|\mathbf{ \Sigma} y\| \end{align}\]

And hence,

\[\| {\bf A} \|_2= \sigma_1\]

In the above equations, all the notations for the norm \(\| . \|\) refer to the \(p=2\) Euclidean norm, and we used the fact that \({\bf U}\) and \({\bf V}\) are orthogonal matrices and hence \(\|{\bf U}\|_2 = \|{\bf V}\|_2 = 1\).

Example:

We begin with the following non-square matrix \({\bf A}\):

\[{\bf A} = \left[ \begin{array}{ccc} 3 & 2 & 3 \\ 8 & 8 & 2 \\ 8 & 7 & 4 \\ 1 & 8 & 7 \\ 6 & 4 & 7 \\ \end{array} \right].\]

The matrix of singular values, \({\bf \Sigma}\), computed from the SVD factorization is:

\[ \Sigma = \left[ \begin{array}{ccc} 20.916 & 0 & 0 \\ 0 & 6.53207 & 0 \\ 0 & 0 & 4.22807 \\ \end{array} \right]. \]

Consequently the 2-norm of \({\bf A}\) is

\[ \|{\bf A}\|_2 = 20.916.\]

Euclidean norm of the inverse of matrices

Following the same derivation as above, we can show that for a full rank \(n \times n\) matrix we have:

\[\| {\bf A}^{-1} \|_2= \frac{1}{\sigma_n}\]

where \({\sigma_n}\) is the smallest singular value.

For non-square matrices, we can use the definition of the pseudoinverse (regardless of the rank):

\[\| {\bf A}^{+} \|_2= \frac{1}{\sigma_r}\]

where \({\sigma_r}\) is the smallest non-zero singular value. Note that for a full rank square matrix, we have \(\| {\bf A}^{+} \|_2 = \| {\bf A}^{-1} \|_2\). An exception of the definition above is the zero matrix. In this case, \(\| {\bf A}^{+} \|_2 = 0\)

2-Norm Condition Number

The 2-norm condition number of a matrix \({\bf A}\) is given by the ratio of its largest singular value to its smallest singular value:

\[\text{cond}_2(A) = \|{\bf A}\|_2 \|{\bf A}^{-1}\|_2 = \sigma_{\max}/\sigma_{\min}.\]

If the matrix \({\bf A}\) is rank deficient, i.e. \(\text{rank}({\bf A}) < \min(m,n)\), then \(\text{cond}_2({\bf A}) = \infty\).

Low-rank Approximation

The best rank-\(k\) approximation for a \(m \times n\) matrix \({\bf A}\), where \(k < s = \min(m,n)\), for some matrix norm \(\|.\|\), is one that minimizes the following problem:

\[\begin{aligned} &\min_{ {\bf A}_k } \ \|{\bf A} - {\bf A}_k\| \\ &\textrm{such that} \quad \mathrm{rank}({\bf A}_k) \le k. \end{aligned}\]

Under the induced \(2\)-norm, the best rank-\(k\) approximation is given by the sum of the first \(k\) outer products of the left and right singular vectors scaled by the corresponding singular value (where, \(\sigma_1 \ge \dots \ge \sigma_s\)):

\[{\bf A}_k = \sigma_1 \bf{u}_1 \bf{v}_1^T + \dots \sigma_k \bf{u}_k \bf{v}_k^T\]

Observe that the norm of the difference between the best approximation and the matrix under the induced \(2\)-norm condition is the magnitude of the \((k+1)^\text{th}\) singular value of the matrix:

\[\|{\bf A} - {\bf A}_k\|_2 = \left|\left|\sum_{i=k+1}^n \sigma_i \bf{u}_i \bf{v}_i^T\right|\right|_2 = \sigma_{k+1}\]

Note that the best rank-\({k}\) approximation to \({\bf A}\) can be stored efficiently by only storing the \({k}\) singular values \({\sigma_1,\dots,\sigma_k}\), the \({k}\) left singular vectors \({\bf u_1,\dots,\bf u_k}\), and the \({k}\) right singular vectors \({\bf v_1,\dots, \bf v_k}\).

The figure below show best rank-\(k\) approximations of an image (you can find the code snippet that generates these images in the IPython notebook):

Review Questions

  1. For a matrix \({\bf A}\) with SVD decomposition \({\bf A} = {\bf U \Sigma V}^T\), what are the columns of \({\bf U}\) and how can we find them? What are the columns of \({\bf V}\) and how can we find them? What are the entries of \({\bf \Sigma}\) and how can we find them?
  2. What special properties are true of \({\bf U}\), \({\bf V}\) and \({\bf \Sigma}\)?
  3. What are the shapes of \({\bf U}\), \({\bf V}\) and \({\bf \Sigma}\) in the full SVD of an \(m \times n\) matrix?
  4. What are the shapes of \({\bf U}\), \({\bf V}\) and \({\bf \Sigma}\) in the reduced SVD of an \(m \times n\) matrix?
  5. What is the cost of computing the SVD?
  6. Given an already computed SVD of a matrix \({\bf A}\), what is the cost of using the SVD to solve a linear system \({\bf A}\bf{x} = \bf{b}\)? How would you use the SVD to solve this system?
  7. How do you use the SVD to compute a low-rank approximation of a matrix? For a small matrix, you should be able to compute a given low rank approximation (i.e. rank-one, rank-two).
  8. Given the SVD of a matrix \({\bf A}\), what is the SVD of \({\bf A}^+\) (the psuedoinverse of \({\bf A}\))?
  9. Given the SVD of a matrix \({\bf A}\), what is the 2-norm of the matrix? What is the 2-norm condition number of the matrix?

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