tf.linalg.tridiagonal_solve

Solves tridiagonal systems of equations.

tf.linalg.tridiagonal_solve(
    diagonals,
    rhs,
    diagonals_format='compact',
    transpose_rhs=False,
    conjugate_rhs=False,
    name=None,
    partial_pivoting=True,
    perturb_singular=False
)

The input can be supplied in various formats: matrix, sequence and compact, specified by the diagonals_format arg.

In matrix format, diagonals must be a tensor of shape [..., M, M], with two inner-most dimensions representing the square tridiagonal matrices. Elements outside of the three diagonals will be ignored.

In sequence format, diagonals are supplied as a tuple or list of three tensors of shapes [..., N], [..., M], [..., N] representing superdiagonals, diagonals, and subdiagonals, respectively. N can be either M-1 or M; in the latter case, the last element of superdiagonal and the first element of subdiagonal will be ignored.

In compact format the three diagonals are brought together into one tensor of shape [..., 3, M], with last two dimensions containing superdiagonals, diagonals, and subdiagonals, in order. Similarly to sequence format, elements diagonals[..., 0, M-1] and diagonals[..., 2, 0] are ignored.

The compact format is recommended as the one with best performance. In case you need to cast a tensor into a compact format manually, use tf.gather_nd. An example for a tensor of shape [m, m]:

rhs = tf.constant([...])
matrix = tf.constant([[...]])
m = matrix.shape[0]
dummy_idx = [0, 0]  # An arbitrary element to use as a dummy
indices = [[[i, i + 1] for i in range(m - 1)] + [dummy_idx],  # Superdiagonal
         [[i, i] for i in range(m)],                          # Diagonal
         [dummy_idx] + [[i + 1, i] for i in range(m - 1)]]    # Subdiagonal
diagonals=tf.gather_nd(matrix, indices)
x = tf.linalg.tridiagonal_solve(diagonals, rhs)

Regardless of the diagonals_format, rhs is a tensor of shape [..., M] or [..., M, K]. The latter allows to simultaneously solve K systems with the same left-hand sides and K different right-hand sides. If transpose_rhs is set to True the expected shape is [..., M] or [..., K, M].

The batch dimensions, denoted as ..., must be the same in diagonals and rhs.

The output is a tensor of the same shape as rhs: either [..., M] or [..., M, K].

The op isn't guaranteed to raise an error if the input matrix is not invertible. tf.debugging.check_numerics can be applied to the output to detect invertibility problems.

On CPU, solution is computed via Gaussian elimination with or without partial pivoting, depending on partial_pivoting parameter. On GPU, Nvidia's cuSPARSE library is used: https://docs.nvidia.com/cuda/cusparse/index.html#gtsv

diagonals A Tensor or tuple of Tensors describing left-hand sides. The shape depends of diagonals_format, see description above. Must be float32, float64, complex64, or complex128.
rhs A Tensor of shape [..., M] or [..., M, K] and with the same dtype as diagonals. Note that if the shape of rhs and/or diags isn't known statically, rhs will be treated as a matrix rather than a vector.
diagonals_format one of matrix, sequence, or compact. Default is compact.
transpose_rhs If True, rhs is transposed before solving (has no effect if the shape of rhs is [..., M]).
conjugate_rhs If True, rhs is conjugated before solving.
name A name to give this Op (optional).
partial_pivoting whether to perform partial pivoting. True by default. Partial pivoting makes the procedure more stable, but slower. Partial pivoting is unnecessary in some cases, including diagonally dominant and symmetric positive definite matrices (see e.g. theorem 9.12 in [1]).
perturb_singular whether to perturb singular matrices to return a finite result. False by default. If true, solutions to systems involving a singular matrix will be computed by perturbing near-zero pivots in the partially pivoted LU decomposition. Specifically, tiny pivots are perturbed by an amount of order eps * max_{ij} |U(i,j)| to avoid overflow. Here U is the upper triangular part of the LU decomposition, and eps is the machine precision. This is useful for solving numerically singular systems when computing eigenvectors by inverse iteration. If partial_pivoting is False, perturb_singular must be False as well.

A Tensor of shape [..., M] or [..., M, K] containing the solutions. If the input matrix is singular, the result is undefined.

ValueError Is raised if any of the following conditions hold:

  1. An unsupported type is provided as input,
  2. the input tensors have incorrect shapes,
  3. perturb_singular is True but partial_pivoting is not.
UnimplementedError Whenever partial_pivoting is true and the backend is XLA, or whenever perturb_singular is true and the backend is XLA or GPU.

[1] Nicholas J. Higham (2002). Accuracy and Stability of Numerical Algorithms: Second Edition. SIAM. p. 175. ISBN 978-0-89871-802-7.