scipy.linalg.lapack.cgelss#
- scipy.linalg.lapack.cgelss(a, b, cond=-1.0, lwork=..., overwrite_a=0, overwrite_b=0) = <flapack function cgelss>#
Solve a least-squares problem by singular value decomposition (LAPACK
cgelss).- Parameters:
- andarray
Matrix of shape
(m, n).- bndarray
Right-hand side(s) with
max(m, n)rows.- condfloat, optional
Singular values below
condtimes the largest are treated as zero. Default is -1.0, which uses machine precision.- lworkint, optional
Size of the workspace. Default is routine-specific. Use
gelss_lworkfor the optimal value.- overwrite_aint, optional
If nonzero, a may be overwritten in place. Default is 0.
- overwrite_bint, optional
If nonzero, b may be overwritten in place. Default is 0.
- Returns:
- vndarray
The first
min(m, n)right singular vectors of a, as LAPACK leaves them.- xndarray
Minimum-norm solution in the first
nrows.- sndarray
Singular values of a, in descending order.
- rankint
Effective rank of a, determined using cond.
- workndarray
Workspace actually used;
work[0]is the optimal lwork.- infoint
0 on success; if negative, the
-info-th argument had an illegal value; if positive, the algorithm failed to converge.