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# tf.linalg.LinearOperatorCirculant2D

`LinearOperator` acting like a block circulant matrix.

``````tf.linalg.LinearOperatorCirculant2D(
spectrum, input_output_dtype=tf.dtypes.complex64, is_non_singular=None,
name='LinearOperatorCirculant2D'
)
``````

This operator acts like a block circulant 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 N` matrix. This matrix `A` is not materialized, but for purposes of broadcasting this shape will be relevant.

#### Description in terms of block circulant matrices

If `A` is block circulant, with block sizes `N0, N1` (`N0 * N1 = N`): `A` has a block circulant structure, composed of `N0 x N0` blocks, with each block an `N1 x N1` circulant matrix.

For example, with `W`, `X`, `Y`, `Z` each circulant,

``````A = |W Z Y X|
|X W Z Y|
|Y X W Z|
|Z Y X W|
``````

Note that `A` itself will not in general be circulant.

#### Description in terms of the frequency spectrum

There is an equivalent description in terms of the [batch] spectrum `H` and Fourier transforms. Here we consider `A.shape = [N, N]` and ignore batch dimensions.

If `H.shape = [N0, N1]`, (`N0 * N1 = N`): Loosely speaking, matrix multiplication is equal to the action of a Fourier multiplier: `A u = IDFT2[ H DFT2[u] ]`. Precisely speaking, given `[N, R]` matrix `u`, let `DFT2[u]` be the `[N0, N1, R]` `Tensor` defined by re-shaping `u` to `[N0, N1, R]` and taking a two dimensional DFT across the first two dimensions. Let `IDFT2` be the inverse of `DFT2`. Matrix multiplication may be expressed columnwise:

```
```

#### Operator properties deduced from the spectrum.

• This operator is positive definite if and only if `Real{H} > 0`.

A general property of Fourier transforms is the correspondence between Hermitian functions and real valued transforms.

Suppose `H.shape = [B1,...,Bb, N0, N1]`, we say that `H` is a Hermitian spectrum if, with `%` indicating modulus division,

``````H[..., n0 % N0, n1 % N1] = ComplexConjugate[ H[..., (-n0) % N0, (-n1) % N1 ].
``````
• This operator corresponds to a real matrix if and only if `H` is Hermitian.
• This operator is self-adjoint if and only if `H` is real.

See e.g. "Discrete-Time Signal Processing", Oppenheim and Schafer.

### Example of a self-adjoint positive definite operator

``````# spectrum is real ==> operator is self-adjoint
# spectrum is positive ==> operator is positive definite
spectrum = [[1., 2., 3.],
[4., 5., 6.],
[7., 8., 9.]]

operator = LinearOperatorCirculant2D(spectrum)

# IFFT[spectrum]
operator.convolution_kernel()
==> [[5.0+0.0j, -0.5-.3j, -0.5+.3j],
[-1.5-.9j,        0,        0],
[-1.5+.9j,        0,        0]]

operator.to_dense()
==> Complex self adjoint 9 x 9 matrix.
``````

#### Example of defining in terms of a real convolution kernel,

``````# convolution_kernel is real ==> spectrum is Hermitian.
convolution_kernel = [[1., 2., 1.], [5., -1., 1.]]
spectrum = tf.signal.fft2d(tf.cast(convolution_kernel, tf.complex64))

# spectrum is shape [2, 3] ==> operator is shape [6, 6]
# spectrum is Hermitian ==> operator is real.
operator = LinearOperatorCirculant2D(spectrum, input_output_dtype=tf.float32)
``````

#### Performance

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

• `operator.matmul(x)` is `O(R*N*Log[N])`
• `operator.solve(x)` is `O(R*N*Log[N])`
• `operator.determinant()` involves a size `N` `reduce_prod`.

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.

#### Args:

• `spectrum`: Shape `[B1,...,Bb, N]` `Tensor`. Allowed dtypes: `float16`, `float32`, `float64`, `complex64`, `complex128`. Type can be different than `input_output_dtype`
• `input_output_dtype`: `dtype` for input/output.
• `is_non_singular`: Expect that this operator is non-singular.
• `is_self_adjoint`: Expect that this operator is equal to its hermitian transpose. If `spectrum` is real, this will always be 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: 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 to prepend to all ops created by this class.

#### Attributes:

• `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 operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns `TensorShape([B1,...,Bb])`, equivalent to `A.shape[:-2]`

• `block_depth`: Depth of recursively defined circulant blocks defining this `Operator`.

With `A` the dense representation of this `Operator`,

`block_depth = 1` means `A` is symmetric circulant. For example,

``````A = |w z y x|
|x w z y|
|y x w z|
|z y x w|
``````

`block_depth = 2` means `A` is block symmetric circulant with symemtric circulant blocks. For example, with `W`, `X`, `Y`, `Z` symmetric circulant,

``````A = |W Z Y X|
|X W Z Y|
|Y X W Z|
|Z Y X W|
``````

`block_depth = 3` means `A` is block symmetric circulant with block symmetric circulant blocks.

• `block_shape`
• `domain_dimension`: Dimension (in the sense of vector spaces) of the domain of this operator.

If this operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns `N`.

• `dtype`: The `DType` of `Tensor`s handled by this `LinearOperator`.

• `graph_parents`: List of graph dependencies of this `LinearOperator`. (deprecated)

• `is_non_singular`

• `is_positive_definite`

• `is_self_adjoint`

• `is_square`: Return `True/False` depending on if this operator is square.

• `range_dimension`: Dimension (in the sense of vector spaces) of the range of this operator.

If this operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns `M`.

• `shape`: `TensorShape` of this `LinearOperator`.

If this operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns `TensorShape([B1,...,Bb, M, N])`, equivalent to `A.shape`.

• `spectrum`

• `tensor_rank`: Rank (in the sense of tensors) of matrix corresponding to this operator.

If this operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns `b + 2`.

## Methods

### `add_to_tensor`

View source

``````add_to_tensor(
)
``````

Add matrix represented by this operator to `x`. Equivalent to `A + x`.

#### Args:

• `x`: `Tensor` with same `dtype` and shape broadcastable to `self.shape`.
• `name`: A name to give this `Op`.

#### Returns:

A `Tensor` with broadcast shape and same `dtype` as `self`.

### `adjoint`

View source

``````adjoint(
)
``````

Returns the adjoint of the current `LinearOperator`.

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

#### Args:

• `name`: A name for this `Op`.

#### Returns:

`LinearOperator` which represents the adjoint of this `LinearOperator`.

### `assert_hermitian_spectrum`

View source

``````assert_hermitian_spectrum(
name='assert_hermitian_spectrum'
)
``````

Returns an `Op` that asserts this operator has Hermitian spectrum.

This operator corresponds to a real-valued matrix if and only if its spectrum is Hermitian.

#### Args:

• `name`: A name to give this `Op`.

#### Returns:

An `Op` that asserts this operator has Hermitian spectrum.

### `assert_non_singular`

View source

``````assert_non_singular(
name='assert_non_singular'
)
``````

Returns an `Op` that asserts this operator is non singular.

This operator is considered non-singular if

``````ConditionNumber < max{100, range_dimension, domain_dimension} * eps,
eps := np.finfo(self.dtype.as_numpy_dtype).eps
``````

#### Args:

• `name`: A string name to prepend to created ops.

#### Returns:

An `Assert` `Op`, that, when run, will raise an `InvalidArgumentError` if the operator is singular.

### `assert_positive_definite`

View source

``````assert_positive_definite(
name='assert_positive_definite'
)
``````

Returns an `Op` that asserts this operator is positive definite.

Here, positive definite means that 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.

#### Args:

• `name`: A name to give this `Op`.

#### Returns:

An `Assert` `Op`, that, when run, will raise an `InvalidArgumentError` if the operator is not positive definite.

### `assert_self_adjoint`

View source

``````assert_self_adjoint(
)
``````

Returns an `Op` that asserts this operator is self-adjoint.

Here we check that this operator is exactly equal to its hermitian transpose.

#### Args:

• `name`: A string name to prepend to created ops.

#### Returns:

An `Assert` `Op`, that, when run, will raise an `InvalidArgumentError` if the operator is not self-adjoint.

### `batch_shape_tensor`

View source

``````batch_shape_tensor(
name='batch_shape_tensor'
)
``````

Shape of batch dimensions of this operator, determined at runtime.

If this operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns a `Tensor` holding `[B1,...,Bb]`.

#### Args:

• `name`: A name for this `Op`.

#### Returns:

`int32` `Tensor`

### `block_shape_tensor`

View source

``````block_shape_tensor()
``````

Shape of the block dimensions of `self.spectrum`.

### `cholesky`

View source

``````cholesky(
name='cholesky'
)
``````

Returns a Cholesky factor as a `LinearOperator`.

Given `A` representing this `LinearOperator`, if `A` is positive definite self-adjoint, return `L`, where `A = L L^T`, i.e. the cholesky decomposition.

#### Args:

• `name`: A name for this `Op`.

#### Returns:

`LinearOperator` which represents the lower triangular matrix in the Cholesky decomposition.

#### Raises:

• `ValueError`: When the `LinearOperator` is not hinted to be positive definite and self adjoint.

### `convolution_kernel`

View source

``````convolution_kernel(
name='convolution_kernel'
)
``````

Convolution kernel corresponding to `self.spectrum`.

The `D` dimensional DFT of this kernel is the frequency domain spectrum of this operator.

#### Args:

• `name`: A name to give this `Op`.

#### Returns:

`Tensor` with `dtype` `self.dtype`.

### `determinant`

View source

``````determinant(
name='det'
)
``````

Determinant for every batch member.

#### Args:

• `name`: A name for this `Op`.

#### Returns:

`Tensor` with shape `self.batch_shape` and same `dtype` as `self`.

#### Raises:

• `NotImplementedError`: If `self.is_square` is `False`.

### `diag_part`

View source

``````diag_part(
name='diag_part'
)
``````

Efficiently get the [batch] diagonal part of this operator.

If this operator has shape `[B1,...,Bb, M, N]`, this returns a `Tensor` `diagonal`, of shape `[B1,...,Bb, min(M, N)]`, where `diagonal[b1,...,bb, i] = self.to_dense()[b1,...,bb, i, i]`.

``````my_operator = LinearOperatorDiag([1., 2.])

# Efficiently get the diagonal
my_operator.diag_part()
==> [1., 2.]

# Equivalent, but inefficient method
tf.linalg.diag_part(my_operator.to_dense())
==> [1., 2.]
``````

#### Args:

• `name`: A name for this `Op`.

#### Returns:

• `diag_part`: A `Tensor` of same `dtype` as self.

### `domain_dimension_tensor`

View source

``````domain_dimension_tensor(
name='domain_dimension_tensor'
)
``````

Dimension (in the sense of vector spaces) of the domain of this operator.

Determined at runtime.

If this operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns `N`.

#### Args:

• `name`: A name for this `Op`.

#### Returns:

`int32` `Tensor`

### `eigvals`

View source

``````eigvals(
name='eigvals'
)
``````

Returns the eigenvalues of this linear operator.

If the operator is marked as self-adjoint (via `is_self_adjoint`) this computation can be more efficient.

#### Args:

• `name`: A name for this `Op`.

#### Returns:

Shape `[B1,...,Bb, N]` `Tensor` of same `dtype` as `self`.

### `inverse`

View source

``````inverse(
name='inverse'
)
``````

Returns the Inverse of this `LinearOperator`.

Given `A` representing this `LinearOperator`, return a `LinearOperator` representing `A^-1`.

#### Args:

• `name`: A name scope to use for ops added by this method.

#### Returns:

`LinearOperator` representing inverse of this matrix.

#### Raises:

• `ValueError`: When the `LinearOperator` is not hinted to be `non_singular`.

### `log_abs_determinant`

View source

``````log_abs_determinant(
name='log_abs_det'
)
``````

Log absolute value of determinant for every batch member.

#### Args:

• `name`: A name for this `Op`.

#### Returns:

`Tensor` with shape `self.batch_shape` and same `dtype` as `self`.

#### Raises:

• `NotImplementedError`: If `self.is_square` is `False`.

### `matmul`

View source

``````matmul(
)
``````

Transform [batch] matrix `x` with left multiplication: `x --> Ax`.

``````# Make an operator acting like batch matrix A.  Assume A.shape = [..., M, N]
operator = LinearOperator(...)
operator.shape = [..., M, N]

X = ... # shape [..., N, R], batch matrix, R > 0.

Y = operator.matmul(X)
Y.shape
==> [..., M, R]

Y[..., :, r] = sum_j A[..., :, j] X[j, r]
``````

#### Args:

• `x`: `LinearOperator` or `Tensor` with compatible shape and same `dtype` as `self`. See class docstring for definition of compatibility.
• `adjoint`: Python `bool`. If `True`, left multiply by the adjoint: `A^H x`.
• `adjoint_arg`: Python `bool`. If `True`, compute `A x^H` where `x^H` is the hermitian transpose (transposition and complex conjugation).
• `name`: A name for this `Op`.

#### Returns:

A `LinearOperator` or `Tensor` with shape `[..., M, R]` and same `dtype` as `self`.

### `matvec`

View source

``````matvec(
)
``````

Transform [batch] vector `x` with left multiplication: `x --> Ax`.

``````# Make an operator acting like batch matric A.  Assume A.shape = [..., M, N]
operator = LinearOperator(...)

X = ... # shape [..., N], batch vector

Y = operator.matvec(X)
Y.shape
==> [..., M]

Y[..., :] = sum_j A[..., :, j] X[..., j]
``````

#### Args:

• `x`: `Tensor` with compatible shape and same `dtype` as `self`. `x` is treated as a [batch] vector meaning for every set of leading dimensions, the last dimension defines a vector. See class docstring for definition of compatibility.
• `adjoint`: Python `bool`. If `True`, left multiply by the adjoint: `A^H x`.
• `name`: A name for this `Op`.

#### Returns:

A `Tensor` with shape `[..., M]` and same `dtype` as `self`.

### `range_dimension_tensor`

View source

``````range_dimension_tensor(
name='range_dimension_tensor'
)
``````

Dimension (in the sense of vector spaces) of the range of this operator.

Determined at runtime.

If this operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns `M`.

#### Args:

• `name`: A name for this `Op`.

#### Returns:

`int32` `Tensor`

### `shape_tensor`

View source

``````shape_tensor(
name='shape_tensor'
)
``````

Shape of this `LinearOperator`, determined at runtime.

If this operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns a `Tensor` holding `[B1,...,Bb, M, N]`, equivalent to `tf.shape(A)`.

#### Args:

• `name`: A name for this `Op`.

#### Returns:

`int32` `Tensor`

### `solve`

View source

``````solve(
)
``````

Solve (exact or approx) `R` (batch) systems of equations: `A X = rhs`.

The returned `Tensor` will be close to an exact solution if `A` is well conditioned. Otherwise closeness will vary. See class docstring for details.

#### Examples:

``````# Make an operator acting like batch matrix A.  Assume A.shape = [..., M, N]
operator = LinearOperator(...)
operator.shape = [..., M, N]

# Solve R > 0 linear systems for every member of the batch.
RHS = ... # shape [..., M, R]

X = operator.solve(RHS)
# X[..., :, r] is the solution to the r'th linear system
# sum_j A[..., :, j] X[..., j, r] = RHS[..., :, r]

operator.matmul(X)
==> RHS
``````

#### Args:

• `rhs`: `Tensor` with same `dtype` as this operator and compatible shape. `rhs` is treated like a [batch] matrix meaning for every set of leading dimensions, the last two dimensions defines a matrix. See class docstring for definition of compatibility.
• `adjoint`: Python `bool`. If `True`, solve the system involving the adjoint of this `LinearOperator`: `A^H X = rhs`.
• `adjoint_arg`: Python `bool`. If `True`, solve `A X = rhs^H` where `rhs^H` is the hermitian transpose (transposition and complex conjugation).
• `name`: A name scope to use for ops added by this method.

#### Returns:

`Tensor` with shape `[...,N, R]` and same `dtype` as `rhs`.

#### Raises:

• `NotImplementedError`: If `self.is_non_singular` or `is_square` is False.

### `solvevec`

View source

``````solvevec(
)
``````

Solve single equation with best effort: `A X = rhs`.

The returned `Tensor` will be close to an exact solution if `A` is well conditioned. Otherwise closeness will vary. See class docstring for details.

#### Examples:

``````# Make an operator acting like batch matrix A.  Assume A.shape = [..., M, N]
operator = LinearOperator(...)
operator.shape = [..., M, N]

# Solve one linear system for every member of the batch.
RHS = ... # shape [..., M]

X = operator.solvevec(RHS)
# X is the solution to the linear system
# sum_j A[..., :, j] X[..., j] = RHS[..., :]

operator.matvec(X)
==> RHS
``````

#### Args:

• `rhs`: `Tensor` with same `dtype` as this operator. `rhs` is treated like a [batch] vector meaning for every set of leading dimensions, the last dimension defines a vector. See class docstring for definition of compatibility regarding batch dimensions.
• `adjoint`: Python `bool`. If `True`, solve the system involving the adjoint of this `LinearOperator`: `A^H X = rhs`.
• `name`: A name scope to use for ops added by this method.

#### Returns:

`Tensor` with shape `[...,N]` and same `dtype` as `rhs`.

#### Raises:

• `NotImplementedError`: If `self.is_non_singular` or `is_square` is False.

### `tensor_rank_tensor`

View source

``````tensor_rank_tensor(
name='tensor_rank_tensor'
)
``````

Rank (in the sense of tensors) of matrix corresponding to this operator.

If this operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns `b + 2`.

#### Args:

• `name`: A name for this `Op`.

#### Returns:

`int32` `Tensor`, determined at runtime.

### `to_dense`

View source

``````to_dense(
name='to_dense'
)
``````

Return a dense (batch) matrix representing this operator.

### `trace`

View source

``````trace(
name='trace'
)
``````

Trace of the linear operator, equal to sum of `self.diag_part()`.

If the operator is square, this is also the sum of the eigenvalues.

#### Args:

• `name`: A name for this `Op`.

#### Returns:

Shape `[B1,...,Bb]` `Tensor` of same `dtype` as `self`.