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Multiplies tridiagonal matrix by matrix.
tf.linalg.tridiagonal_matmul(
diagonals, rhs, diagonals_format='compact', name=None
)
diagonals
is representation of 3-diagonal NxN matrix, which depends on
diagonals_format
.
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.
If sequence
format, diagonals
is list or tuple of three tensors:
[superdiag, maindiag, subdiag]
, each having shape [..., M]. Last element
of superdiag
first element of subdiag
are 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 sequence
format is recommended as the one with the best performance.
rhs
is matrix to the right of multiplication. It has shape [..., M, N]
.
Example:
superdiag = tf.constant([-1, -1, 0], dtype=tf.float64)
maindiag = tf.constant([2, 2, 2], dtype=tf.float64)
subdiag = tf.constant([0, -1, -1], dtype=tf.float64)
diagonals = [superdiag, maindiag, subdiag]
rhs = tf.constant([[1, 1], [1, 1], [1, 1]], dtype=tf.float64)
x = tf.linalg.tridiagonal_matmul(diagonals, rhs, diagonals_format='sequence')
Returns | |
---|---|
A Tensor of shape [..., M, N] containing the result of multiplication.
|
Raises | |
---|---|
ValueError
|
An unsupported type is provided as input, or when the input tensors have incorrect shapes. |