tf.matrix_triangular_solve

Aliases:

  • tf.linalg.triangular_solve
  • tf.matrix_triangular_solve
tf.matrix_triangular_solve(
    matrix,
    rhs,
    lower=True,
    adjoint=False,
    name=None
)

Defined in generated file: tensorflow/python/ops/gen_linalg_ops.py.

See the guide: Math > Matrix Math Functions

Solves systems of linear equations with upper or lower triangular matrices by

backsubstitution.

matrix is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. If lower is True then the strictly upper triangular part of each inner-most matrix is assumed to be zero and not accessed. If lower is False then the strictly lower triangular part of each inner-most matrix is assumed to be zero and not accessed. rhs is a tensor of shape [..., M, K].

The output is a tensor of shape [..., M, K]. If adjoint is True then the innermost matrices in output satisfy matrix equations matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]. If adjoint is False then the strictly then the innermost matrices in output satisfy matrix equations adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j].

Args:

  • matrix: A Tensor. Must be one of the following types: float64, float32, complex64, complex128. Shape is [..., M, M].
  • rhs: A Tensor. Must have the same type as matrix. Shape is [..., M, K].
  • lower: An optional bool. Defaults to True. Boolean indicating whether the innermost matrices in matrix are lower or upper triangular.
  • adjoint: An optional bool. Defaults to False. Boolean indicating whether to solve with matrix or its (block-wise) adjoint.

  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as matrix.

Numpy Compatibility

Equivalent to scipy.linalg.solve_triangular