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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)

Solve the linear system $ax=b$ 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 $(\ast ,M,M)$ where $\ast$ is zero or more batch dimensions.

• b (Tensor) – A Tensor of shape $(\ast ,M)$ or $(\ast ,M,N)$. Right-hand side matrix in $ax=b$.

• 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 $(\ast ,M)$ or $(\ast ,M,N)$, which is the solution to the system $ax=b$. 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.]