tf.linalg.LinearOperatorTridiag

View source on GitHub

LinearOperator acting like a [batch] square tridiagonal matrix.

Inherits From: LinearOperator

tf.linalg.LinearOperatorTridiag(
    diagonals, diagonals_format=_COMPACT, is_non_singular=None,
    is_self_adjoint=None, is_positive_definite=None, is_square=None,
    name='LinearOperatorTridiag'
)

This operator acts like a [batch] square tridiagonal matrix A with shape [B1,...,Bb, N, N] for some b >= 0. The first b indices index a batch member. For every batch index (i1,...,ib), A[i1,...,ib, : :] is an N x M matrix. This matrix A is not materialized, but for purposes of broadcasting this shape will be relevant.

Example usage:

Create a 3 x 3 tridiagonal linear operator.

superdiag = [3., 4., 5.]
diag = [1., -1., 2.]
subdiag = [6., 7., 8]
operator = tf.linalg.LinearOperatorTridiag(
   [superdiag, diag, subdiag],
   diagonals_format='sequence')
operator.to_dense()
<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[ 1.,  3.,  0.],
       [ 7., -1.,  4.],
       [ 0.,  8.,  2.]], dtype=float32)>
operator.shape
TensorShape([3, 3])

Scalar Tensor output.

operator.log_abs_determinant()
<tf.Tensor: shape=(), dtype=float32, numpy=4.3307333>

Create a [2, 3] batch of 4 x 4 linear operators.

diagonals = tf.random.normal(shape=[2, 3, 3, 4])
operator = tf.linalg.LinearOperatorTridiag(
  diagonals,
  diagonals_format='compact')

Create a shape [2, 1, 4, 2] vector. Note that this shape is compatible since the batch dimensions, [2, 1], are broadcast to operator.batch_shape = [2, 3].

y = tf.random.normal(shape=[2, 1, 4, 2])
x = operator.solve(y)
x
<tf.Tensor: shape=(2, 3, 4, 2), dtype=float32, numpy=...,
dtype=float32)>

Shape compatibility

This operator acts on [batch] matrix with compatible shape. x is a batch matrix with compatible shape for matmul and solve if

operator.shape = [B1,...,Bb] + [N, N],  with b >= 0
x.shape =   [C1,...,Cc] + [N, R],
and [C1,...,Cc] broadcasts with [B1,...,Bb].

Performance

Suppose operator is a LinearOperatorTridiag of shape [N, N], and x.shape = [N, R]. Then

  • operator.matmul(x) will take O(N * R) time.
  • operator.solve(x) will take O(N * R) time.

If instead operator and x have shape [B1,...,Bb, N, N] and [B1,...,Bb, N, R], every operation increases in complexity by B1*...*Bb.

Matrix property hints

This LinearOperator is initialized with boolean flags of the form is_X, for X = non_singular, self_adjoint, positive_definite, square. These have the following meaning:

  • If is_X == True, callers should expect the operator to have the property X. This is a promise that should be fulfilled, but is not a runtime assert. For example, finite floating point precision may result in these promises being violated.
  • If is_X == False, callers should expect the operator to not have X.
  • If is_X == None (the default), callers should have no expectation either way.

diagonals Tensor or list of Tensors depending on diagonals_format.

If diagonals_format=sequence, this is a list of three Tensor's each with shape [B1, ..., Bb, N], b >= 0, N >= 0, representing the superdiagonal, diagonal and subdiagonal in that order. Note the superdiagonal is padded with an element in the last position, and the subdiagonal is padded with an element in the front.

If diagonals_format=matrix this is a [B1, ... Bb, N, N] shaped Tensor representing the full tridiagonal matrix.

If diagonals_format=compact this is a [B1, ... Bb, 3, N] shaped Tensor with the second to last dimension indexing the superdiagonal, diagonal and subdiagonal in that order. Note the superdiagonal is padded with an element in the last position, and the subdiagonal is padded with an element in the front.

In every case, these Tensors are all floating dtype.

diagonals_format one of matrix, sequence, or compact. Default is compact.
is_non_singular Expect that this operator is non-singular.
is_self_adjoint Expect that this operator is equal to its hermitian transpose. If diag.dtype is real, this is auto-set to True.
is_positive_definite Expect that this operator is positive definite, meaning the quadratic form x^H A x has positive real part for all nonzero x. Note that we do not require the operator to be self-adjoint to be positive-definite. See: https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices
is_square Expect that this operator acts like square [batch] matrices.
name A name for this LinearOperator.

TypeError If diag.dtype is not an allowed type.
ValueError If diag.dtype is real, and is_self_adjoint is not True.

H Returns the adjoint of the current LinearOperator.

Given A representing this LinearOperator, return A*. Note that calling self.adjoint() and self.H are equivalent.

batch_shape TensorShape of batch dimensions of this LinearOperator.

If this op