mindspore.scipy.linalg.solve_triangular

mindspore.scipy.linalg.solve_triangular(a, b, trans=0, lower=False, unit_diagonal=False, overwrite_b=False, debug=None, check_finite=True)[source]

Solve the linear system \(a x = b\) for x, Assuming a is a triangular matrix.

Note

  • solve_triangular is currently only used in mindscience scientific computing scenarios and dose not support other usage scenarios.

  • solve_triangular is not supported on Windows platform yet.

Parameters

  • a (Tensor) – A triangular matrix of shape \((*, M, M)\) where \(*\) is zero or more batch dimensions.

  • b (Tensor) – A Tensor of shape \((*, M)\) or \((*, M, N)\). Right-hand side matrix in \(a x = b\).

  • trans (Union[int, str], optional) –

    Type of system to solve. Default: 0.

    trans

    system

    0 or ‘N’

    a x = b

    1 or ‘T’

    a^T x = b

    2 or ‘C’

    a^H x = b

  • lower (bool, optional) – Use only data contained in the lower triangle of a. Default: False.

  • unit_diagonal (bool, optional) – If True, diagonal elements of \(a\) are assumed to be 1 and will not be referenced. Default: False.

  • overwrite_b (bool, optional) – Not implemented now. Default: False.

  • debug (Any, optional) – Not implemented now. Default: None.

  • check_finite (bool, optional) – Not implemented now. Default: True.

Returns

Tensor of shape \((*, M)\) or \((*, M, N)\), which is the solution to the system \(a x = b\). Shape of \(x\) matches \(b\).

Raises

  • ValueError – If a is less than 2 dimension.

  • ValueError – if a is not square matrix.

  • TypeError – If dtype of a and b are not the same.

  • ValueError – If the shape of a and b are not matched.

  • ValueError – If trans is not in set {0, 1, 2, ‘N’, ‘T’, ‘C’}.

Supported Platforms:

Ascend CPU

Examples

>>> import numpy as onp
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore.scipy.linalg import solve_triangular
>>> a = Tensor(onp.array([[3, 0, 0, 0], [2, 1, 0, 0], [1, 0, 1, 0], [1, 1, 1, 1]], onp.float32))
>>> b = Tensor(onp.array([3, 1, 3, 4], onp.float32))
>>> x = solve_triangular(a, b, lower=True, unit_diagonal=False, trans='N')
>>> print(x)
[ 1. -1.  2.  2.]
>>> print(a @ x)  # Check the result
[3. 1. 3. 4.]