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Milds # Singular value

In mathematics, in particular functional analysis, the singular values, or s-numbers of a compact operator T : XY acting between Hilbert spaces X and Y, are the square roots of non-negative eigenvalues of the self-adjoint operator T*T (where T* denotes the adjoint of T).

The singular values are non-negative real numbers, usually listed in decreasing order (s1(T), s2(T), …). The largest singular value s1(T) is equal to the operator norm of T (see Min-max theorem).

Visualisation of a singular value decomposition (SVD) of a 2-dimensional, real shearing matrix M. First, we see the unit disc in blue together with the two canonical unit vectors. We then see the action of M, which distorts the disc to an ellipse. The SVD decomposes M into three simple transformations: a rotation V*, a scaling Σ along the rotated coordinate axes and a second rotation U. Σ is a diagonal matrix containing in its diagonal the singular values of M, which represent the lengths σ1 and σ2 of the semi-axes of the ellipse.

In the case that T acts on euclidean space Rn, there is a simple geometric interpretation for the singular values: Consider the image by T of the unit sphere; this is an ellipsoid, and the lengths of its semi-axes are the singular values of T (the figure provides an example in R2).

The singular values are the absolute values of the eigenvalues of a normal matrix A, because the spectral theorem can be applied to obtain unitary diagonalization of A as A = UΛU*. Therefore, ${\sqrt {A^{*}A}}={\sqrt {U\Lambda ^{2}U^{*}}}=U\ |\Lambda |\ U^{*}$ .

Most norms on Hilbert space operators studied are defined using s-numbers. For example, the Ky Fan-k-norm is the sum of first k singular values, the trace norm is the sum of all singular values, and the Schatten norm is the pth root of the sum of the pth powers of the singular values. Note that each norm is defined only on a special class of operators, hence s-numbers are useful in classifying different operators.

In the finite-dimensional case, a matrix can always be decomposed in the form UΣV*, where U and V* are unitary matrices and Σ is a diagonal matrix with the singular values lying on the diagonal. This is the singular value decomposition.

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• ✪ Singular Value Decomposition (the SVD)
• ✪ Lecture 47 — Singular Value Decomposition | Stanford University
• ✪ Singular matrices | Matrices | Precalculus | Khan Academy
• ✪ Linear Algebra - Lecture 42 - The Singular Value Decomposition
• ✪ L8B: The Structured Singular Value

## Basic properties

For $A\in \mathbb {C} ^{m\times n},$ and $i=1,2,\ldots ,\min\{m,n\}$ .

Min-max theorem for singular values. Here $U:\dim(U)=i$ is a subspace of $\mathbb {C} ^{n}$ of dimension $i$ .

{\begin{aligned}\sigma _{i}(A)&=\min _{\dim(U)=n-i+1}\max _{\underset {\|x\|_{2}=1}{x\in U}}\left\|Ax\right\|_{2}.\\\sigma _{i}(A)&=\max _{\dim(U)=i}\min _{\underset {\|x\|_{2}=1}{x\in U}}\left\|Ax\right\|_{2}.\end{aligned}} Matrix transpose and conjugate do not alter singular values.

$\sigma _{i}(A)=\sigma _{i}\left(A^{\textsf {T}}\right)=\sigma _{i}\left(A^{*}\right)=\sigma _{i}\left({\bar {A}}\right).$ For any unitary $U\in \mathbb {C} ^{m\times m},V\in \mathbb {C} ^{n\times n}.$ $\sigma _{i}(A)=\sigma _{i}(UAV).$ Relation to eigenvalues:

$\sigma _{i}^{2}(A)=\lambda _{i}\left(AA^{*}\right)=\lambda _{i}\left(A^{*}A\right).$ ### Singular values of sub-matrices

For $A\in \mathbb {C} ^{m\times n}.$ 1. Let $B$ denote $A$ with one of its rows or columns deleted. Then
$\sigma _{i+1}(A)\leq \sigma _{i}(B)\leq \sigma _{i}(A)$ 2. Let $B$ denote $A$ with one of its rows and columns deleted. Then
$\sigma _{i+2}(A)\leq \sigma _{i}(B)\leq \sigma _{i}(A)$ 3. Let $B$ denote an $(m-k)\times (n-l)$ submatrix of $A$ . Then
$\sigma _{i+k+l}(A)\leq \sigma _{i}(B)\leq \sigma _{i}(A)$ ### Singular values of $A+B$ For $A,B\in \mathbb {C} ^{m\times n}$ 1. $\sum _{i=1}^{k}\sigma _{i}(A+B)\leq \sum _{i=1}^{k}\sigma _{i}(A)+\sigma _{i}(B),\quad k=\min\{m,n\}$ 2. $\sigma _{i+j-1}(A+B)\leq \sigma _{i}(A)+\sigma _{j}(B).\quad i,j\in \mathbb {N} ,\ i+j-1\leq \min\{m,n\}$ ### Singular values of $AB$ For $A,B\in \mathbb {C} ^{n\times n}$ 1. {\begin{aligned}\prod _{i=n}^{i=n-k+1}\sigma _{i}(A)\sigma _{i}(B)&\leq \prod _{i=n}^{i=n-k+1}\sigma _{i}(AB)\\\prod _{i=1}^{k}\sigma _{i}(AB)&\leq \prod _{i=1}^{k}\sigma _{i}(A)\sigma _{i}(B),\\\sum _{i=1}^{k}\sigma _{i}^{p}(AB)&\leq \sum _{i=1}^{k}\sigma _{i}^{p}(A)\sigma _{i}^{p}(B),\end{aligned}} 2. $\sigma _{n}(A)\sigma _{i}(B)\leq \sigma _{i}(AB)\leq \sigma _{1}(A)\sigma _{i}(B)\quad i=1,2,\ldots ,n.$ For $A,B\in \mathbb {C} ^{m\times n}$ $2\sigma _{i}(AB^{*})\leq \sigma _{i}\left(A^{*}A+B^{*}B\right),\quad i=1,2,\ldots ,n.$ ### Singular values and eigenvalues

For $A\in \mathbb {C} ^{n\times n}$ .

1. See
$\lambda _{i}\left(A+A^{*}\right)\leq 2\sigma _{i}(A),\quad i=1,2,\ldots ,n.$ 2. Assume $\left|\lambda _{1}(A)\right|\geq \cdots \geq \left|\lambda _{n}(A)\right|$ . Then for $k=1,2,\ldots ,n$ :
1. Weyl's theorem
$\prod _{i=1}^{k}\left|\lambda _{i}(A)\right|\leq \prod _{i=1}^{k}\sigma _{i}(A).$ 2. For $p>0$ .
$\sum _{i=1}^{k}\left|\lambda _{i}^{p}(A)\right|\leq \sum _{i=1}^{k}\sigma _{i}^{p}(A).$ ## History

This concept was introduced by Erhard Schmidt in 1907. Schmidt called singular values "eigenvalues" at that time. The name "singular value" was first quoted by Smithies in 1937. In 1957, Allahverdiev proved the following characterization of the nth s-number :

$s_{n}(T)=\inf {\big \{}\,\|T-L\|:L{\text{ is an operator of finite rank }} This formulation made it possible to extend the notion of s-numbers to operators in Banach space.