cupyx.scipy.linalg.solve_triangular#

cupyx.scipy.linalg.solve_triangular(a, b, trans=0, lower=False, unit_diagonal=False, overwrite_b=False, check_finite=False)[source]#

Solve the equation a x = b for x, assuming a is a triangular matrix.

Parameters:

a (cupy.ndarray) – The matrix with dimension (..., M, M).
b (cupy.ndarray) – The matrix with dimension (..., M,) or (..., M, N).
lower (bool) – Use only data contained in the lower triangle of a. Default is to use upper triangle.
trans (0, 1, 2, 'N', 'T' or 'C') –
Type of system to solve:
- ’0’ or ‘N’ – \(a x = b\)
- ’1’ or ‘T’ – \(a^T x = b\)
- ’2’ or ‘C’ – \(a^H x = b\)
unit_diagonal (bool) – If True, diagonal elements of a are assumed to be 1 and will not be referenced.
overwrite_b (bool) – Allow overwriting data in b (may enhance performance)
check_finite (bool) – 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.

Returns:

The matrix with dimension (..., M,) or (..., M, N).

Return type:

cupy.ndarray

Note

Unlike the SciPy counterpart, the CuPy implementation supports batches of matrices.