- scipy.linalg.lstsq(a, b, cond=None, overwrite_a=False, overwrite_b=False, check_finite=True)¶
Compute least-squares solution to equation Ax = b.
Compute a vector x such that the 2-norm |b - A x| is minimized.
a : (M, N) array_like
Left hand side matrix (2-D array).
b : (M,) or (M, K) array_like
Right hand side matrix or vector (1-D or 2-D array).
cond : float, optional
Cutoff for ‘small’ singular values; used to determine effective rank of a. Singular values smaller than rcond * largest_singular_value are considered zero.
overwrite_a : bool, optional
Discard data in a (may enhance performance). Default is False.
overwrite_b : bool, optional
Discard data in b (may enhance performance). Default is False.
check_finite : bool, optional
Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.
x : (N,) or (N, K) ndarray
Least-squares solution. Return shape matches shape of b.
residues : () or (1,) or (K,) ndarray
Sums of residues, squared 2-norm for each column in b - a x. If rank of matrix a is < N or > M this is an empty array. If b was 1-D, this is an (1,) shape array, otherwise the shape is (K,).
rank : int
Effective rank of matrix a.
s : (min(M,N),) ndarray
Singular values of a. The condition number of a is abs(s/s[-1]).
If computation does not converge.
- linear least squares with non-negativity constraint