# svd¶

## Purpose¶

Computes the singular values of a matrix.

## Format¶

s = svd(x)
Parameters: x (NxP matrix) – matrix whose singular values are to be computed s (Mx1 vector) – where $$M = min(N,P)$$, containing the singular values of x arranged in descending order.

## Global Input¶

_svderr

scalar, if the singular values cannot be computed, _svderr will be nonzero.

## Examples¶

// Create a 10x3 matrix
x = {  -0.60     3.50     0.47,
8.40    16.50     0.27,
11.40     6.50     0.17,
7.40    -0.50    -2.43,
-9.60   -10.50     0.57,
-17.60    -5.50     0.67,
-12.60   -14.50     0.87,
18.40    12.50    -1.43,
-11.60   -19.50     0.77,
6.40    11.50     0.07 };

// Calculate the singular values
s = svd(x);


After the code above, s will be equal to:

49.58
14.96
2.24


## Remarks¶

1. svd() is not thread-safe. New code should use svds() instead.

2. Error handling is controlled with the low bit of the trap flag.

 trap 0 set _svderr to a non-zero value and terminate with message trap 1 set _svderr to a non-zero value and continue execution

svd.src