# tf.linalg.LinearOperatorIdentity

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|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator_identity.py#L99-L501) |

`LinearOperator` acting like a \[batch\] square identity matrix.

Inherits From: [`LinearOperator`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/linalg/LinearOperator), [`Module`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Module)

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.linalg.LinearOperatorIdentity`](https://www.tensorflow.org/api_docs/python/tf/linalg/LinearOperatorIdentity)

<br />

    tf.linalg.LinearOperatorIdentity(
        num_rows,
        batch_shape=None,
        dtype=None,
        is_non_singular=True,
        is_self_adjoint=True,
        is_positive_definite=True,
        is_square=True,
        assert_proper_shapes=False,
        name='LinearOperatorIdentity'
    )

This operator acts like a \[batch\] identity 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.

`LinearOperatorIdentity` is initialized with `num_rows`, and optionally
`batch_shape`, and `dtype` arguments. If `batch_shape` is `None`, this
operator efficiently passes through all arguments. If `batch_shape` is
provided, broadcasting may occur, which will require making copies.

    # Create a 2 x 2 identity matrix.
    operator = LinearOperatorIdentity(num_rows=2, dtype=tf.float32)

    operator.to_dense()
    ==> [[1., 0.]
         [0., 1.]]

    operator.shape
    ==> [2, 2]

    operator.log_abs_determinant()
    ==> 0.

    x = ... Shape [2, 4] Tensor
    operator.matmul(x)
    ==> Shape [2, 4] Tensor, same as x.

    y = tf.random.normal(shape=[3, 2, 4])
    # Note that y.shape is compatible with operator.shape because operator.shape
    # is broadcast to [3, 2, 2].
    # This broadcast does NOT require copying data, since we can infer that y
    # will be passed through without changing shape.  We are always able to infer
    # this if the operator has no batch_shape.
    x = operator.solve(y)
    ==> Shape [3, 2, 4] Tensor, same as y.

    # Create a 2-batch of 2x2 identity matrices
    operator = LinearOperatorIdentity(num_rows=2, batch_shape=[2])
    operator.to_dense()
    ==> [[[1., 0.]
          [0., 1.]],
         [[1., 0.]
          [0., 1.]]]

    # Here, even though the operator has a batch shape, the input is the same as
    # the output, so x can be passed through without a copy.  The operator is able
    # to detect that no broadcast is necessary because both x and the operator
    # have statically defined shape.
    x = ... Shape [2, 2, 3]
    operator.matmul(x)
    ==> Shape [2, 2, 3] Tensor, same as x

    # Here the operator and x have different batch_shape, and are broadcast.
    # This requires a copy, since the output is different size than the input.
    x = ... Shape [1, 2, 3]
    operator.matmul(x)
    ==> Shape [2, 2, 3] Tensor, equal to [x, x]

### 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] to [D1,...,Dd]

### Performance

If `batch_shape` initialization arg is `None`:

- `operator.matmul(x)` is `O(1)`
- `operator.solve(x)` is `O(1)`
- `operator.determinant()` is `O(1)`

If `batch_shape` initialization arg is provided, and static checks cannot
rule out the need to broadcast:

- `operator.matmul(x)` is `O(D1*...*Dd*N*R)`
- `operator.solve(x)` is `O(D1*...*Dd*N*R)`
- `operator.determinant()` is `O(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.

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| ## Args ||
|---|---|
| `num_rows` | Scalar non-negative integer `Tensor`. Number of rows in the corresponding identity matrix. |
| `batch_shape` | Optional `1-D` integer `Tensor`. The shape of the leading dimensions. If `None`, this operator has no leading dimensions. |
| `dtype` | Data type of the matrix that this operator represents. |
| `is_non_singular` | Expect that this operator is non-singular. |
| `is_self_adjoint` | Expect that this operator is equal to its hermitian transpose. |
| `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. |
| `assert_proper_shapes` | Python `bool`. If `False`, only perform static checks that initialization and method arguments have proper shape. If `True`, and static checks are inconclusive, add asserts to the graph. |
| `name` | A name for this `LinearOperator` |

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| ## Raises ||
|---|---|
| `ValueError` | If `num_rows` is determined statically to be non-scalar, or negative. |
| `ValueError` | If `batch_shape` is determined statically to not be 1-D, or negative. |
| `ValueError` | If any of the following is not `True`: `{is_self_adjoint, is_non_singular, is_positive_definite}`. |
| `TypeError` | If `num_rows` or `batch_shape` is ref-type (e.g. Variable). |

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| ## Attributes ||
|---|---|
| `H` | Returns the adjoint of the current `LinearOperator`. <br /> 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`. <br /> 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]` |
| `domain_dimension` | Dimension (in the sense of vector spaces) of the domain of this operator. <br /> 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) <br /> > [!WARNING] > **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Do not call `graph_parents`. |
| `is_non_singular` | <br /> |
| `is_positive_definite` | <br /> <br /> |
| `is_self_adjoint` | <br /> <br /> |
| `is_square` | Return `True/False` depending on if this operator is square. |
| `parameters` | Dictionary of parameters used to instantiate this `LinearOperator`. |
| `range_dimension` | Dimension (in the sense of vector spaces) of the range of this operator. <br /> 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`. <br /> 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`. |
| `tensor_rank` | Rank (in the sense of tensors) of matrix corresponding to this operator. <br /> If this operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns `b + 2`. |

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## Methods

### `add_to_tensor`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator_identity.py#L398-L414)

    add_to_tensor(
        mat, name='add_to_tensor'
    )

Add matrix represented by this operator to `mat`. Equiv to `I + mat`.

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| Args ||
|---|---|
| `mat` | `Tensor` with same `dtype` and shape broadcastable to `self`. |
| `name` | A name to give this `Op`. |

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

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### `adjoint`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L981-L996)

    adjoint(
        name='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.

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| Args ||
|---|---|
| `name` | A name for this `Op`. |

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| Returns ||
|---|---|
| `LinearOperator` which represents the adjoint of this `LinearOperator`. ||

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### `assert_non_singular`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L585-L603)

    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

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| Args ||
|---|---|
| `name` | A string name to prepend to created ops. |

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

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### `assert_positive_definite`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L621-L636)

    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.

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| Args ||
|---|---|
| `name` | A name to give this `Op`. |

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

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### `assert_self_adjoint`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L648-L662)

    assert_self_adjoint(
        name='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.

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| Args ||
|---|---|
| `name` | A string name to prepend to created ops. |

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

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### `batch_shape_tensor`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L397-L412)

    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]`.

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| Args ||
|---|---|
| `name` | A name for this `Op`. |

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| Returns ||
|---|---|
| `int32` `Tensor` ||

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### `cholesky`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L1026-L1049)

    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.

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| Args ||
|---|---|
| `name` | A name for this `Op`. |

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

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| Raises ||
|---|---|
| `ValueError` | When the `LinearOperator` is not hinted to be positive definite and self adjoint. |

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### `cond`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L1175-L1185)

    cond(
        name='cond'
    )

Returns the condition number of this linear operator.

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| Args ||
|---|---|
| `name` | A name for this `Op`. |

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

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### `determinant`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L781-L798)

    determinant(
        name='det'
    )

Determinant for every batch member.

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| Args ||
|---|---|
| `name` | A name for this `Op`. |

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| Returns ||
|---|---|
| `Tensor` with shape `self.batch_shape` and same `dtype` as `self`. ||

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| Raises ||
|---|---|
| `NotImplementedError` | If `self.is_square` is `False`. |

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### `diag_part`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L1076-L1102)

    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.]

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| Args ||
|---|---|
| `name` | A name for this `Op`. |

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| Returns ||
|---|---|
| `diag_part` | A `Tensor` of same `dtype` as self. |

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### `domain_dimension_tensor`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L484-L500)

    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`.

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| Args ||
|---|---|
| `name` | A name for this `Op`. |

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| Returns ||
|---|---|
| `int32` `Tensor` ||

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### `eigvals`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L1143-L1160)

    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.

> [!NOTE]
> **Note:** This currently only supports self-adjoint operators.

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| Args ||
|---|---|
| `name` | A name for this `Op`. |

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

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### `inverse`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L1001-L1024)

    inverse(
        name='inverse'
    )

Returns the Inverse of this `LinearOperator`.

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

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| Args ||
|---|---|
| `name` | A name scope to use for ops added by this method. |

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| Returns ||
|---|---|
| `LinearOperator` representing inverse of this matrix. ||

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| Raises ||
|---|---|
| `ValueError` | When the `LinearOperator` is not hinted to be `non_singular`. |

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### `log_abs_determinant`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L810-L827)

    log_abs_determinant(
        name='log_abs_det'
    )

Log absolute value of determinant for every batch member.

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| Args ||
|---|---|
| `name` | A name for this `Op`. |

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| Returns ||
|---|---|
| `Tensor` with shape `self.batch_shape` and same `dtype` as `self`. ||

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| Raises ||
|---|---|
| `NotImplementedError` | If `self.is_square` is `False`. |

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### `matmul`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L675-L728)

    matmul(
        x, adjoint=False, adjoint_arg=False, name='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]

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| 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`. |

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

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### `matvec`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L738-L771)

    matvec(
        x, adjoint=False, name='matvec'
    )

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

    # Make an operator acting like batch matrix 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]

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| 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`. |

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

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### `range_dimension_tensor`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L528-L544)

    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`.

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| Args ||
|---|---|
| `name` | A name for this `Op`. |

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| Returns ||
|---|---|
| `int32` `Tensor` ||

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### `shape_tensor`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L363-L381)

    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)`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/shape).

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| Args ||
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| `name` | A name for this `Op`. |

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| Returns ||
|---|---|
| `int32` `Tensor` ||

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### `solve`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L848-L923)

    solve(
        rhs, adjoint=False, adjoint_arg=False, name='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

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| 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. |

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

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| Raises ||
|---|---|
| `NotImplementedError` | If `self.is_non_singular` or `is_square` is False. |

<br />

### `solvevec`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L931-L979)

    solvevec(
        rhs, adjoint=False, name='solve'
    )

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

<br />

<br />

<br />

| 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. |

<br />

<br />

<br />

<br />

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

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `NotImplementedError` | If `self.is_non_singular` or `is_square` is False. |

<br />

### `tensor_rank_tensor`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L441-L455)

    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`.

<br />

<br />

<br />

| Args ||
|---|---|
| `name` | A name for this `Op`. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| `int32` `Tensor`, determined at runtime. ||

<br />

### `to_dense`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L1067-L1070)

    to_dense(
        name='to_dense'
    )

Return a dense (batch) matrix representing this operator.

### `trace`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L1107-L1119)

    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.

<br />

<br />

<br />

| Args ||
|---|---|
| `name` | A name for this `Op`. |

<br />

<br />

<br />

<br />

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

<br />

### `__getitem__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator_identity.py#L495-L501)

    __getitem__(
        slices
    )

### `__matmul__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/linalg/linear_operator.py#L730-L731)

    __matmul__(
        other
    )