Scilab Function

rankqr - rank revealing QR factorization

Calling Sequence

[Q,R,JPVT,RANK,SVAL]=rankqr(A, [RCOND,JPVT])

Parameters

Description

To compute (optionally) a rank-revealing QR factorization of a 
real general M-by-N real or complex matrix  A,  which may be rank-deficient,
and estimate its effective rank using incremental condition 
estimation.

The routine uses a QR factorization with column pivoting:
   A * P = Q * R,  where  R = [ R11 R12 ],
                              [  0  R22 ]
with R11 defined as the largest leading submatrix whose estimated
condition number is less than 1/RCOND.  The order of R11, RANK,
is the effective rank of A.
   
If the triangular factorization is a rank-revealing one
(which will be the case if the leading columns were well-
conditioned), then SVAL(1) will also be an estimate for
the largest singular value of A, and SVAL(2) and SVAL(3)
will be estimates for the RANK-th and (RANK+1)-st singular
values of A, respectively.
By examining these values, one can confirm that the rank
is well defined with respect to the chosen value of RCOND.
The ratio SVAL(1)/SVAL(2) is an estimate of the condition
number of R(1:RANK,1:RANK).
   

REFERENCE

Examples

See Also