- scipy.linalg.solve(a, b, sym_pos=False, lower=False, overwrite_a=False, overwrite_b=False, debug=None, check_finite=True, assume_a='gen', transposed=False)¶
Solves the linear equation set
a * x = bfor the unknown
If the data matrix is known to be a particular type then supplying the corresponding string to
assume_akey chooses the dedicated solver. The available options are
'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.
- a(N, N) array_like
Square input data
- b(N, NRHS) array_like
Input data for the right hand side.
- sym_posbool, optional
Assume a is symmetric and positive definite. This key is deprecated and assume_a = ‘pos’ keyword is recommended instead. The functionality is the same. It will be removed in the future.
- lowerbool, optional
If True, only the data contained in the lower triangle of a. Default is to use upper triangle. (ignored for
- overwrite_abool, optional
Allow overwriting data in a (may enhance performance). Default is False.
- overwrite_bbool, optional
Allow overwriting data in b (may enhance performance). Default is False.
- check_finitebool, 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.
- assume_astr, optional
Valid entries are explained above.
- transposed: bool, optional
a^T x = bfor real matrices, raises NotImplementedError for complex matrices (only for True).
- x(N, NRHS) ndarray
The solution array.
If size mismatches detected or input a is not square.
If the matrix is singular.
If an ill-conditioned input a is detected.
If transposed is True and input a is a complex matrix.
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.
Given a and b, solve for x:
>>> 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)