scipy.linalg.solve

scipy.linalg.solve(a, b, sym_pos=False, lower=False, overwrite_a=False, overwrite_b=False, check_finite=True, assume_a='gen', transposed=False)

Solves the linear equation set a @ x == b for the unknown x for square a matrix.

If the data matrix is known to be a particular type then supplying the corresponding string to assume_a key chooses the dedicated solver. The available options are

generic matrix	‘gen’
symmetric	‘sym’
hermitian	‘her’
positive definite	‘pos’

If omitted, 'gen' is the default structure.

The datatype of the arrays define which solver is called regardless of the values. In other words, even when the complex array entries have precisely zero imaginary parts, the complex solver will be called based on the data type of the array.

Parameters:

a(N, N) array_like

Square input data

b(N, NRHS) array_like

Input data for the right hand side.

sym_posbool, default: False, deprecated

Assume a is symmetric and positive definite.

Deprecated since version 0.19.0: This keyword is deprecated and should be replaced by using assume_a = 'pos'. sym_pos will be removed in SciPy 1.11.0.

lowerbool, default: False

Ignored if assume_a == 'gen' (the default). If True, the calculation uses only the data in the lower triangle of a; entries above the diagonal are ignored. If False (default), the calculation uses only the data in the upper triangle of a; entries below the diagonal are ignored.

overwrite_abool, default: False

Allow overwriting data in a (may enhance performance).

overwrite_bbool, default: False

Allow overwriting data in b (may enhance performance).

check_finitebool, default: True

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.

assume_astr, {‘gen’, ‘sym’, ‘her’, ‘pos’}

Valid entries are explained above.

transposedbool, default: False

If True, solve a.T @ x == b. Raises NotImplementedError for complex a.

Returns:

x(N, NRHS) ndarray

The solution array.

Raises:

ValueError

If size mismatches detected or input a is not square.

LinAlgError

If the matrix is singular.

LinAlgWarning

If an ill-conditioned input a is detected.

NotImplementedError

If transposed is True and input a is a complex matrix.

Notes

If the input b matrix is a 1-D array with N elements, when supplied together with an NxN input a, it is assumed as a valid column vector despite the apparent size mismatch. This is compatible with the numpy.dot() behavior and the returned result is still 1-D array.

The generic, symmetric, Hermitian and positive definite solutions are obtained via calling ?GESV, ?SYSV, ?HESV, and ?POSV routines of LAPACK respectively.

Examples

Given a and b, solve for x:

>>> import numpy as np
>>> a = np.array([[3, 2, 0], [1, -1, 0], [0, 5, 1]])
>>> b = np.array([2, 4, -1])
>>> from scipy import linalg
>>> x = linalg.solve(a, b)
>>> x
array([ 2., -2.,  9.])
>>> np.dot(a, x) == b
array([ True,  True,  True], dtype=bool)