# svd2¶

## Purpose¶

Computes the singular value decomposition of a matrix so that: $$x = u * s * v'$$ (compact u).

## Format¶

{ u, s, v } = svd2(x)
Parameters: x (NxP matrix) – matrix whose singular values are to be computed u (NxN or NxP matrix) – the left singular vectors of x. If $$N > P$$, then u will be $$NxP$$, containing only the $$P$$ left singular vectors of x. s (NxP or PxP diagonal matrix) – contains the singular values of x arranged in descending order on the principal diagonal. If $$N > P$$, then s will be $$PxP$$. v (PxP matrix) – the right singular vectors of x.

## 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
{ u, s, v } = svd2(x);


After the code above, u, s and v will be equal to:

u =  0.04     0.20    -0.11
0.36     0.38    -0.14
0.25    -0.23    -0.44
0.10    -0.39     0.75
-0.29    -0.04    -0.06
-0.33     0.57     0.35
-0.39    -0.08    -0.14
0.44    -0.29     0.10
-0.44    -0.37    -0.25
0.26     0.24    -0.07

s = 49.58     0.00     0.00
0.00    14.96     0.00
0.00     0.00     2.24

v =  0.70    -0.70    -0.10
0.71     0.70     0.05
-0.04     0.10    -0.99


## Remarks¶

1. svd2() is not thread-safe. New code should use svdcusv() instead.

2. Error handling is controlled with the low bit of the trap flag. If the singular values cannot be computed, _svderr will be set to a non-zero value.

 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