`scipy.linalg._flapack:dgelss`

Signature

def dgelss(a, b, cond = -1.0, lwork = Ellipsis, overwrite_a = 0, overwrite_b = 0)

Solve a least-squares problem by singular value decomposition (LAPACK dgelss).

a : ndarray: Matrix of shape (m, n).
b : ndarray: Right-hand side(s) with max(m, n) rows.
cond : float, optional: Singular values below cond times the largest are treated as zero. Default is -1.0, which uses machine precision.
lwork : int, optional: Size of the workspace. Default is routine-specific. Use gelss_lwork for the optimal value.
overwrite_a : int, optional: If nonzero, a may be overwritten in place. Default is 0.
overwrite_b : int, optional: If nonzero, b may be overwritten in place. Default is 0.

v : ndarray: The first min(m, n) right singular vectors of a, as LAPACK leaves them.
x : ndarray: Minimum-norm solution in the first n rows.
s : ndarray: Singular values of a, in descending order.
rank : int: Effective rank of a, determined using cond.
work : ndarray: Workspace actually used; work[0] is the optimal lwork.
info : int: 0 on success; if negative, the -info-th argument had an illegal value; if positive, the algorithm failed to converge.