jax.scipy.linalg.solve#
- jax.scipy.linalg.solve(a, b, sym_pos=False, lower=False, overwrite_a=False, overwrite_b=False, debug=False, check_finite=True, assume_a='gen')[source]#
Solves the linear equation set
a @ x == b
for the unknownx
LAX-backend implementation of
scipy.linalg._basic.solve()
.Does not support the Scipy argument
check_finite=True
, because compiled JAX code cannot perform checks of array values at runtime.Does not support the Scipy argument
overwrite_*=True
.Original docstring below.
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 aregeneric 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_pos (bool, 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.lower (bool, 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.assume_a (str, {'gen', 'sym', 'her', 'pos'}) – Valid entries are explained above.
overwrite_a (
bool
) –overwrite_b (
bool
) –debug (
bool
) –check_finite (
bool
) –
- Returns:
x – The solution array.
- Return type:
(N, NRHS) ndarray