Singular Value Decomposition
A = U $\cdot $W $\cdot $V${}^{T}$ 
where:
A  is any $m$ (rows) $\times n$ (columns) matrix, where $m\ge n$  
U  is an $m\times n$ columnorthogonal matrix  
W  is an $n\times n$ diagonal matrix with ${w}_{ii}\ge 0$  
V${}^{T}$  is the transpose of an $n\times n$ orthogonal matrix 
CALL sla_SVD (M, N, MP, NP, A, W, V, WORK, JSTAT)
M,N  I  $m$, $n$, the numbers of rows and columns in matrix A 
 
MP,NP  I  physical dimensions of array containing matrix A 


A  D(MP,NP)  array containing $m\times n$ matrix A 
A  D(MP,NP)  array containing $m\times n$ columnorthogonal matrix U 
 
W  D(N)  $n\times n$ diagonal matrix W (diagonal elements only) 
 
V  D(NP,NP)  array containing $n\times n$ orthogonal matrix V (n.b. not V${}^{T}$) 


WORK  D(N)  workspace 


JSTAT  I  0 = OK, $$1 = array A wrong shape, $>$0 = index of W for which convergence failed (see note 3, below) 