scipy.linalg.lstsq(a, b, cond=None, overwrite_a=False, overwrite_b=False)[source]

Compute least-squares solution to equation Ax = b.

Compute a vector x such that the 2-norm |b - A x| is minimized.

Parameters :

a : array, shape (M, N)

Left hand side matrix (2-D array).

b : array, shape (M,) or (M, K)

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.

Returns :

x : array, shape (N,) or (N, K) depending on shape of b

Least-squares solution.

residues : ndarray, shape () or (1,) or (K,)

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 : array, shape (min(M,N),)

Singular values of a. The condition number of a is abs(s[0]/s[-1]).

Raises :

LinAlgError : :

If computation does not converge.

See also

linear least squares with non-negativity constraint

Previous topic


Next topic