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TensorFlow 1 version
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View source on GitHub
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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')
Args | |
|---|---|
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, N] and with the same dtype as diagonals.
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diagonals_format
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one of sequence, or compact. Default is compact.
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name
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A name to give this Op (optional).
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Returns | |
|---|---|
A Tensor of shape [..., M, N] containing the result of multiplication.
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Raises | |
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ValueError
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An unsupported type is provided as input, or when the input tensors have incorrect shapes. |