SVD(<matrix>)
This function performs a Singular Value Decomposition on an m × n matrix. The result is two matrices and a
vector:
{[[m × m square orthogonal]],[[n × n square orthogonal]],[real]}.
SVL(<matrix>)
This function returns a vector containing the singular values of the supplied matrix.
TRACE(<matrix>)
This function finds the trace of a square matrix. The trace is equal to the sum of the diagonal elements or the
sum of the eigenvalues.
TRN(matrix)
This function returns the transpose of an n x m matrix.
⎡
2
3
⎤
⎢
For example, if
M
1
1
2
⎥
⎥
then TRN(M1) would return
⎡
2
1
0
⎤
.
⎣
−
⎥
= ⎢ −
⎢
3
2
4
⎦
⎢
0
4
⎥⎦
⎣
201
