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Scilab manual >> Linear Algebra > svd

# svd

singular value decomposition

### Calling Sequence

```s=svd(X)
[U,S,V]=svd(X)
[U,S,V]=svd(X,0) (obsolete)
[U,S,V]=svd(X,"e")
[U,S,V,rk]=svd(X [,tol])```

### Arguments

X

a real or complex matrix

s

real vector (singular values)

S

real diagonal matrix (singular values)

U,V

orthogonal or unitary square matrices (singular vectors).

tol

real number

### Description

`[U,S,V] = svd(X)` produces a diagonal matrix `S` , of the same dimension as `X` and with nonnegative diagonal elements in decreasing order, and unitary matrices `U` and `V` so that `X = U*S*V'`.

`[U,S,V] = svd(X,0)` produces the "economy size" decomposition. If `X` is m-by-n with m > n, then only the first n columns of `U` are computed and `S` is n-by-n.

`s = svd(X)` by itself, returns a vector `s` containing the singular values.

`[U,S,V,rk]=svd(X,tol)` gives in addition `rk`, the numerical rank of `X` i.e. the number of singular values larger than `tol`.

The default value of `tol` is the same as in `rank`.

### Examples

```X=rand(4,2)*rand(2,4)
svd(X)
sqrt(spec(X*X'))```