EVOLUTION-MANAGER

Edit File: linear_operator.py

# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Base class for linear operators."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import abc
import contextlib

import numpy as np
import six

from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.module import module
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.linalg import linalg_impl as linalg
from tensorflow.python.ops.linalg import linear_operator_algebra
from tensorflow.python.ops.linalg import linear_operator_util
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import deprecation
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export

__all__ = ["LinearOperator"]

# TODO(langmore) Use matrix_solve_ls for singular or non-square matrices.
@tf_export("linalg.LinearOperator")
@six.add_metaclass(abc.ABCMeta)
class LinearOperator(module.Module):
  """Base class defining a [batch of] linear operator[s].

Subclasses of `LinearOperator` provide access to common methods on a
  (batch) matrix, without the need to materialize the matrix.  This allows:

* Matrix free computations
  * Operators that take advantage of special structure, while providing a
    consistent API to users.

#### Subclassing

To enable a public method, subclasses should implement the leading-underscore
  version of the method.  The argument signature should be identical except for
  the omission of `name="..."`.  For example, to enable
  `matmul(x, adjoint=False, name="matmul")` a subclass should implement
  `_matmul(x, adjoint=False)`.

#### Performance contract

Subclasses should only implement the assert methods
  (e.g. `assert_non_singular`) if they can be done in less than `O(N^3)`
  time.

Class docstrings should contain an explanation of computational complexity.
  Since this is a high-performance library, attention should be paid to detail,
  and explanations can include constants as well as Big-O notation.

#### Shape compatibility

`LinearOperator` subclasses should operate on a [batch] matrix with
  compatible shape.  Class docstrings should define what is meant by compatible
  shape.  Some subclasses may not support batching.

Examples:

`x` is a batch matrix with compatible shape for `matmul` if

```
  operator.shape = [B1,...,Bb] + [M, N],  b >= 0,
  x.shape =   [B1,...,Bb] + [N, R]
  ```

`rhs` is a batch matrix with compatible shape for `solve` if

```
  operator.shape = [B1,...,Bb] + [M, N],  b >= 0,
  rhs.shape =   [B1,...,Bb] + [M, R]
  ```

#### Example docstring for subclasses.

This operator acts like a (batch) matrix `A` with shape
  `[B1,...,Bb, M, N]` for some `b >= 0`.  The first `b` indices index a
  batch member.  For every batch index `(i1,...,ib)`, `A[i1,...,ib, : :]` is
  an `m x n` matrix.  Again, this matrix `A` may not be materialized, but for
  purposes of identifying and working with compatible arguments the shape is
  relevant.

Examples:

```python
  some_tensor = ... shape = ????
  operator = MyLinOp(some_tensor)

operator.shape()
  ==> [2, 4, 4]

operator.log_abs_determinant()
  ==> Shape [2] Tensor

x = ... Shape [2, 4, 5] Tensor

operator.matmul(x)
  ==> Shape [2, 4, 5] Tensor
  ```

#### Shape compatibility

This operator acts on batch matrices with compatible shape.
  FILL IN WHAT IS MEANT BY COMPATIBLE SHAPE

#### Performance

FILL THIS IN

#### 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.

#### Initialization parameters

All subclasses of `LinearOperator` are expected to pass a `parameters`
  argument to `super().__init__()`.  This should be a `dict` containing
  the unadulterated arguments passed to the subclass `__init__`.  For example,
  `MyLinearOperator` with an initializer should look like:

```python
  def __init__(self, operator, is_square=False, name=None):
     parameters = dict(
         operator=operator,
         is_square=is_square,
         name=name
     )
     ...
     super().__init__(..., parameters=parameters)
   ```

Users can then access `my_linear_operator.parameters` to see all arguments
   passed to its initializer.
  """

# TODO(b/143910018) Remove graph_parents in V3.
  @deprecation.deprecated_args(None, "Do not pass `graph_parents`.  They will "
                               " no longer be used.", "graph_parents")
  def __init__(self,
               dtype,
               graph_parents=None,
               is_non_singular=None,
               is_self_adjoint=None,
               is_positive_definite=None,
               is_square=None,
               name=None,
               parameters=None):
    r"""Initialize the `LinearOperator`.

**This is a private method for subclass use.**
    **Subclasses should copy-paste this `__init__` documentation.**

Args:
      dtype: The type of the this `LinearOperator`.  Arguments to `matmul` and
        `solve` will have to be this type.
      graph_parents: (Deprecated) Python list of graph prerequisites of this
        `LinearOperator` Typically tensors that are passed during initialization
      is_non_singular:  Expect that this operator is non-singular.
      is_self_adjoint:  Expect that this operator is equal to its hermitian
        transpose.  If `dtype` is real, this is equivalent to being symmetric.
      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`.
      parameters: Python `dict` of parameters used to instantiate this
        `LinearOperator`.

Raises:
      ValueError:  If any member of graph_parents is `None` or not a `Tensor`.
      ValueError:  If hints are set incorrectly.
    """
    # Check and auto-set flags.
    if is_positive_definite:
      if is_non_singular is False:
        raise ValueError("A positive definite matrix is always non-singular.")
      is_non_singular = True

if is_non_singular:
      if is_square is False:
        raise ValueError("A non-singular matrix is always square.")
      is_square = True

if is_self_adjoint:
      if is_square is False:
        raise ValueError("A self-adjoint matrix is always square.")
      is_square = True

self._is_square_set_or_implied_by_hints = is_square

if graph_parents is not None:
      self._set_graph_parents(graph_parents)
    else:
      self._graph_parents = []
    self._dtype = dtypes.as_dtype(dtype).base_dtype if dtype else dtype
    self._is_non_singular = is_non_singular
    self._is_self_adjoint = is_self_adjoint
    self._is_positive_definite = is_positive_definite
    self._parameters = self._no_dependency(parameters)
    self._parameters_sanitized = False
    self._name = name or type(self).__name__

@contextlib.contextmanager
  def _name_scope(self, name=None):
    """Helper function to standardize op scope."""
    full_name = self.name
    if name is not None:
      full_name += "/" + name
    with ops.name_scope(full_name) as scope:
      yield scope

@property
  def parameters(self):
    """Dictionary of parameters used to instantiate this `LinearOperator`."""
    return dict(self._parameters)

@property
  def dtype(self):
    """The `DType` of `Tensor`s handled by this `LinearOperator`."""
    return self._dtype

@property
  def name(self):
    """Name prepended to all ops created by this `LinearOperator`."""
    return self._name

@property
  @deprecation.deprecated(None, "Do not call `graph_parents`.")
  def graph_parents(self):
    """List of graph dependencies of this `LinearOperator`."""
    return self._graph_parents

@property
  def is_non_singular(self):
    return self._is_non_singular

@property
  def is_self_adjoint(self):
    return self._is_self_adjoint

@property
  def is_positive_definite(self):
    return self._is_positive_definite

@property
  def is_square(self):
    """Return `True/False` depending on if this operator is square."""
    # Static checks done after __init__.  Why?  Because domain/range dimension
    # sometimes requires lots of work done in the derived class after init.
    auto_square_check = self.domain_dimension == self.range_dimension
    if self._is_square_set_or_implied_by_hints is False and auto_square_check:
      raise ValueError(
          "User set is_square hint to False, but the operator was square.")
    if self._is_square_set_or_implied_by_hints is None:
      return auto_square_check

return self._is_square_set_or_implied_by_hints

@abc.abstractmethod
  def _shape(self):
    # Write this in derived class to enable all static shape methods.
    raise NotImplementedError("_shape is not implemented.")

@property
  def shape(self):
    """`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`.

Returns:
      `TensorShape`, statically determined, may be undefined.
    """
    return self._shape()

def _shape_tensor(self):
    # This is not an abstractmethod, since we want derived classes to be able to
    # override this with optional kwargs, which can reduce the number of
    # `convert_to_tensor` calls.  See derived classes for examples.
    raise NotImplementedError("_shape_tensor is not implemented.")

def shape_tensor(self, 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`
    """
    with self._name_scope(name):
      # Prefer to use statically defined shape if available.
      if self.shape.is_fully_defined():
        return linear_operator_util.shape_tensor(self.shape.as_list())
      else:
        return self._shape_tensor()

@property
  def batch_shape(self):
    """`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]`

Returns:
      `TensorShape`, statically determined, may be undefined.
    """
    # Derived classes get this "for free" once .shape is implemented.
    return self.shape[:-2]

def batch_shape_tensor(self, 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`
    """
    # Derived classes get this "for free" once .shape() is implemented.
    with self._name_scope(name):
      return self._batch_shape_tensor()

def _batch_shape_tensor(self, shape=None):
    # `shape` may be passed in if this can be pre-computed in a
    # more efficient manner, e.g. without excessive Tensor conversions.
    if self.batch_shape.is_fully_defined():
      return linear_operator_util.shape_tensor(
          self.batch_shape.as_list(), name="batch_shape")
    else:
      shape = self.shape_tensor() if shape is None else shape
      return shape[:-2]

@property
  def tensor_rank(self, name="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`.

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

Returns:
      Python integer, or None if the tensor rank is undefined.
    """
    # Derived classes get this "for free" once .shape() is implemented.
    with self._name_scope(name):
      return self.shape.ndims

def tensor_rank_tensor(self, 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.
    """
    # Derived classes get this "for free" once .shape() is implemented.
    with self._name_scope(name):
      return self._tensor_rank_tensor()

def _tensor_rank_tensor(self, shape=None):
    # `shape` may be passed in if this can be pre-computed in a
    # more efficient manner, e.g. without excessive Tensor conversions.
    if self.tensor_rank is not None:
      return ops.convert_to_tensor_v2_with_dispatch(self.tensor_rank)
    else:
      shape = self.shape_tensor() if shape is None else shape
      return array_ops.size(shape)

@property
  def domain_dimension(self):
    """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`.

Returns:
      `Dimension` object.
    """
    # Derived classes get this "for free" once .shape is implemented.
    if self.shape.rank is None:
      return tensor_shape.Dimension(None)
    else:
      return self.shape.dims[-1]

def domain_dimension_tensor(self, 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`
    """
    # Derived classes get this "for free" once .shape() is implemented.
    with self._name_scope(name):
      return self._domain_dimension_tensor()

def _domain_dimension_tensor(self, shape=None):
    # `shape` may be passed in if this can be pre-computed in a
    # more efficient manner, e.g. without excessive Tensor conversions.
    dim_value = tensor_shape.dimension_value(self.domain_dimension)
    if dim_value is not None:
      return ops.convert_to_tensor_v2_with_dispatch(dim_value)
    else:
      shape = self.shape_tensor() if shape is None else shape
      return shape[-1]

@property
  def range_dimension(self):
    """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`.

Returns:
      `Dimension` object.
    """
    # Derived classes get this "for free" once .shape is implemented.
    if self.shape.dims:
      return self.shape.dims[-2]
    else:
      return tensor_shape.Dimension(None)

def range_dimension_tensor(self, 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`
    """
    # Derived classes get this "for free" once .shape() is implemented.
    with self._name_scope(name):
      return self._range_dimension_tensor()

def _range_dimension_tensor(self, shape=None):
    # `shape` may be passed in if this can be pre-computed in a
    # more efficient manner, e.g. without excessive Tensor conversions.
    dim_value = tensor_shape.dimension_value(self.range_dimension)
    if dim_value is not None:
      return ops.convert_to_tensor_v2_with_dispatch(dim_value)
    else:
      shape = self.shape_tensor() if shape is None else shape
      return shape[-2]

def _assert_non_singular(self):
    """Private default implementation of _assert_non_singular."""
    logging.warn(
        "Using (possibly slow) default implementation of assert_non_singular."
        "  Requires conversion to a dense matrix and O(N^3) operations.")
    if self._can_use_cholesky():
      return self.assert_positive_definite()
    else:
      singular_values = linalg_ops.svd(self.to_dense(), compute_uv=False)
      # TODO(langmore) Add .eig and .cond as methods.
      cond = (math_ops.reduce_max(singular_values, axis=-1) /
              math_ops.reduce_min(singular_values, axis=-1))
      return check_ops.assert_less(
          cond,
          self._max_condition_number_to_be_non_singular(),
          message="Singular matrix up to precision epsilon.")

def _max_condition_number_to_be_non_singular(self):
    """Return the maximum condition number that we consider nonsingular."""
    with ops.name_scope("max_nonsingular_condition_number"):
      dtype_eps = np.finfo(self.dtype.as_numpy_dtype).eps
      eps = math_ops.cast(
          math_ops.reduce_max([
              100.,
              math_ops.cast(self.range_dimension_tensor(), self.dtype),
              math_ops.cast(self.domain_dimension_tensor(), self.dtype)
          ]), self.dtype) * dtype_eps
      return 1. / eps

def assert_non_singular(self, 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.
    """
    with self._name_scope(name):
      return self._assert_non_singular()

def _assert_positive_definite(self):
    """Default implementation of _assert_positive_definite."""
    logging.warn(
        "Using (possibly slow) default implementation of "
        "assert_positive_definite."
        "  Requires conversion to a dense matrix and O(N^3) operations.")
    # If the operator is self-adjoint, then checking that
    # Cholesky decomposition succeeds + results in positive diag is necessary
    # and sufficient.
    if self.is_self_adjoint:
      return check_ops.assert_positive(
          array_ops.matrix_diag_part(linalg_ops.cholesky(self.to_dense())),
          message="Matrix was not positive definite.")
    # We have no generic check for positive definite.
    raise NotImplementedError("assert_positive_definite is not implemented.")

def assert_positive_definite(self, 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.
    """
    with self._name_scope(name):
      return self._assert_positive_definite()

def _assert_self_adjoint(self):
    dense = self.to_dense()
    logging.warn(
        "Using (possibly slow) default implementation of assert_self_adjoint."
        "  Requires conversion to a dense matrix.")
    return check_ops.assert_equal(
        dense,
        linalg.adjoint(dense),
        message="Matrix was not equal to its adjoint.")

def assert_self_adjoint(self, 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.

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.
    """
    with self._name_scope(name):
      return self._assert_self_adjoint()

def _check_input_dtype(self, arg):
    """Check that arg.dtype == self.dtype."""
    if arg.dtype.base_dtype != self.dtype:
      raise TypeError(
          "Expected argument to have dtype %s.  Found: %s in tensor %s" %
          (self.dtype, arg.dtype, arg))

@abc.abstractmethod
  def _matmul(self, x, adjoint=False, adjoint_arg=False):
    raise NotImplementedError("_matmul is not implemented.")

def matmul(self, x, adjoint=False, adjoint_arg=False, name="matmul"):
    """Transform [batch] matrix `x` with left multiplication:  `x --> Ax`.

```python
    # 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`.
    """
    if isinstance(x, LinearOperator):
      left_operator = self.adjoint() if adjoint else self
      right_operator = x.adjoint() if adjoint_arg else x

if (right_operator.range_dimension is not None and
          left_operator.domain_dimension is not None and
          right_operator.range_dimension != left_operator.domain_dimension):
        raise ValueError(
            "Operators are incompatible. Expected `x` to have dimension"
            " {} but got {}.".format(
                left_operator.domain_dimension, right_operator.range_dimension))
      with self._name_scope(name):
        return linear_operator_algebra.matmul(left_operator, right_operator)

with self._name_scope(name):
      x = ops.convert_to_tensor_v2_with_dispatch(x, name="x")
      self._check_input_dtype(x)

self_dim = -2 if adjoint else -1
      arg_dim = -1 if adjoint_arg else -2
      tensor_shape.dimension_at_index(
          self.shape, self_dim).assert_is_compatible_with(
              x.shape[arg_dim])

return self._matmul(x, adjoint=adjoint, adjoint_arg=adjoint_arg)

def __matmul__(self, other):
    return self.matmul(other)

def _matvec(self, x, adjoint=False):
    x_mat = array_ops.expand_dims(x, axis=-1)
    y_mat = self.matmul(x_mat, adjoint=adjoint)
    return array_ops.squeeze(y_mat, axis=-1)

def matvec(self, x, adjoint=False, name="matvec"):
    """Transform [batch] vector `x` with left multiplication:  `x --> Ax`.

```python
    # 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]
    ```

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`.
    """
    with self._name_scope(name):
      x = ops.convert_to_tensor_v2_with_dispatch(x, name="x")
      self._check_input_dtype(x)
      self_dim = -2 if adjoint else -1
      tensor_shape.dimension_at_index(
          self.shape, self_dim).assert_is_compatible_with(x.shape[-1])
      return self._matvec(x, adjoint=adjoint)

def _determinant(self):
    logging.warn(
        "Using (possibly slow) default implementation of determinant."
        "  Requires conversion to a dense matrix and O(N^3) operations.")
    if self._can_use_cholesky():
      return math_ops.exp(self.log_abs_determinant())
    return linalg_ops.matrix_determinant(self.to_dense())

def determinant(self, 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`.
    """
    if self.is_square is False:
      raise NotImplementedError(
          "Determinant not implemented for an operator that is expected to "
          "not be square.")
    with self._name_scope(name):
      return self._determinant()

def _log_abs_determinant(self):
    logging.warn(
        "Using (possibly slow) default implementation of determinant."
        "  Requires conversion to a dense matrix and O(N^3) operations.")
    if self._can_use_cholesky():
      diag = array_ops.matrix_diag_part(linalg_ops.cholesky(self.to_dense()))
      return 2 * math_ops.reduce_sum(math_ops.log(diag), axis=[-1])
    _, log_abs_det = linalg.slogdet(self.to_dense())
    return log_abs_det

def log_abs_determinant(self, 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`.

def _dense_solve(self, rhs, adjoint=False, adjoint_arg=False):
    """Solve by conversion to a dense matrix."""
    if self.is_square is False:  # pylint: disable=g-bool-id-comparison
      raise NotImplementedError(
          "Solve is not yet implemented for non-square operators.")
    rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
    if self._can_use_cholesky():
      return linalg_ops.cholesky_solve(
          linalg_ops.cholesky(self.to_dense()), rhs)
    return linear_operator_util.matrix_solve_with_broadcast(
        self.to_dense(), rhs, adjoint=adjoint)

def _solve(self, rhs, adjoint=False, adjoint_arg=False):
    """Default implementation of _solve."""
    logging.warn(
        "Using (possibly slow) default implementation of solve."
        "  Requires conversion to a dense matrix and O(N^3) operations.")
    return self._dense_solve(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg)

def solve(self, 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:

```python
    # 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.
    """
    if self.is_non_singular is False:
      raise NotImplementedError(
          "Exact solve not implemented for an operator that is expected to "
          "be singular.")
    if self.is_square is False:
      raise NotImplementedError(
          "Exact solve not implemented for an operator that is expected to "
          "not be square.")
    if isinstance(rhs, LinearOperator):
      left_operator = self.adjoint() if adjoint else self
      right_operator = rhs.adjoint() if adjoint_arg else rhs

if (right_operator.range_dimension is not None and
          left_operator.domain_dimension is not None and
          right_operator.range_dimension != left_operator.domain_dimension):
        raise ValueError(
            "Operators are incompatible. Expected `rhs` to have dimension"
            " {} but got {}.".format(
                left_operator.domain_dimension, right_operator.range_dimension))
      with self._name_scope(name):
        return linear_operator_algebra.solve(left_operator, right_operator)

with self._name_scope(name):
      rhs = ops.convert_to_tensor_v2_with_dispatch(rhs, name="rhs")
      self._check_input_dtype(rhs)

self_dim = -1 if adjoint else -2
      arg_dim = -1 if adjoint_arg else -2
      tensor_shape.dimension_at_index(
          self.shape, self_dim).assert_is_compatible_with(
              rhs.shape[arg_dim])

return self._solve(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg)

def _solvevec(self, rhs, adjoint=False):
    """Default implementation of _solvevec."""
    rhs_mat = array_ops.expand_dims(rhs, axis=-1)
    solution_mat = self.solve(rhs_mat, adjoint=adjoint)
    return array_ops.squeeze(solution_mat, axis=-1)

def solvevec(self, 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:

```python
    # 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.
    """
    with self._name_scope(name):
      rhs = ops.convert_to_tensor_v2_with_dispatch(rhs, name="rhs")
      self._check_input_dtype(rhs)
      self_dim = -1 if adjoint else -2
      tensor_shape.dimension_at_index(
          self.shape, self_dim).assert_is_compatible_with(rhs.shape[-1])

return self._solvevec(rhs, adjoint=adjoint)

def adjoint(self, 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.

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

Returns:
      `LinearOperator` which represents the adjoint of this `LinearOperator`.
    """
    if self.is_self_adjoint is True:  # pylint: disable=g-bool-id-comparison
      return self
    with self._name_scope(name):
      return linear_operator_algebra.adjoint(self)

# self.H is equivalent to self.adjoint().
  H = property(adjoint, None)

def inverse(self, 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`.
    """
    if self.is_square is False:  # pylint: disable=g-bool-id-comparison
      raise ValueError("Cannot take the Inverse: This operator represents "
                       "a non square matrix.")
    if self.is_non_singular is False:  # pylint: disable=g-bool-id-comparison
      raise ValueError("Cannot take the Inverse: This operator represents "
                       "a singular matrix.")

with self._name_scope(name):
      return linear_operator_algebra.inverse(self)

def cholesky(self, 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.
    """

if not self._can_use_cholesky():
      raise ValueError("Cannot take the Cholesky decomposition: "
                       "Not a positive definite self adjoint matrix.")
    with self._name_scope(name):
      return linear_operator_algebra.cholesky(self)

def _to_dense(self):
    """Generic and often inefficient implementation.  Override often."""
    if self.batch_shape.is_fully_defined():
      batch_shape = self.batch_shape
    else:
      batch_shape = self.batch_shape_tensor()

dim_value = tensor_shape.dimension_value(self.domain_dimension)
    if dim_value is not None:
      n = dim_value
    else:
      n = self.domain_dimension_tensor()

eye = linalg_ops.eye(num_rows=n, batch_shape=batch_shape, dtype=self.dtype)
    return self.matmul(eye)

def to_dense(self, name="to_dense"):
    """Return a dense (batch) matrix representing this operator."""
    with self._name_scope(name):
      return self._to_dense()

def _diag_part(self):
    """Generic and often inefficient implementation.  Override often."""
    return array_ops.matrix_diag_part(self.to_dense())

def diag_part(self, 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.
    """
    with self._name_scope(name):
      return self._diag_part()

def _trace(self):
    return math_ops.reduce_sum(self.diag_part(), axis=-1)

def trace(self, 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`.
    """
    with self._name_scope(name):
      return self._trace()

def _add_to_tensor(self, x):
    # Override if a more efficient implementation is available.
    return self.to_dense() + x

def add_to_tensor(self, x, name="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`.
    """
    with self._name_scope(name):
      x = ops.convert_to_tensor_v2_with_dispatch(x, name="x")
      self._check_input_dtype(x)
      return self._add_to_tensor(x)

def _eigvals(self):
    return linalg_ops.self_adjoint_eigvals(self.to_dense())

def eigvals(self, 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: This currently only supports self-adjoint operators.

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

Returns:
      Shape `[B1,...,Bb, N]` `Tensor` of same `dtype` as `self`.
    """
    if not self.is_self_adjoint:
      raise NotImplementedError("Only self-adjoint matrices are supported.")
    with self._name_scope(name):
      return self._eigvals()

def _cond(self):
    if not self.is_self_adjoint:
      # In general the condition number is the ratio of the
      # absolute value of the largest and smallest singular values.
      vals = linalg_ops.svd(self.to_dense(), compute_uv=False)
    else:
      # For self-adjoint matrices, and in general normal matrices,
      # we can use eigenvalues.
      vals = math_ops.abs(self._eigvals())

return (math_ops.reduce_max(vals, axis=-1) /
            math_ops.reduce_min(vals, axis=-1))

def cond(self, name="cond"):
    """Returns the condition number of this linear operator.

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

Returns:
      Shape `[B1,...,Bb]` `Tensor` of same `dtype` as `self`.
    """
    with self._name_scope(name):
      return self._cond()

def _can_use_cholesky(self):
    return self.is_self_adjoint and self.is_positive_definite

def _set_graph_parents(self, graph_parents):
    """Set self._graph_parents.  Called during derived class init.

This method allows derived classes to set graph_parents, without triggering
    a deprecation warning (which is invoked if `graph_parents` is passed during
    `__init__`.

Args:
      graph_parents: Iterable over Tensors.
    """
    # TODO(b/143910018) Remove this function in V3.
    graph_parents = [] if graph_parents is None else graph_parents
    for i, t in enumerate(graph_parents):
      if t is None or not (linear_operator_util.is_ref(t) or
                           tensor_util.is_tensor(t)):
        raise ValueError("Graph parent item %d is not a Tensor; %s." % (i, t))
    self._graph_parents = graph_parents

# Overrides for tf.linalg functions. This allows a LinearOperator to be used in
# place of a Tensor.
# For instance tf.trace(linop) and linop.trace() both work.

@dispatch.dispatch_for_types(linalg.adjoint, LinearOperator)
def _adjoint(matrix, name=None):
  return matrix.adjoint(name)

@dispatch.dispatch_for_types(linalg.cholesky, LinearOperator)
def _cholesky(input, name=None):   # pylint:disable=redefined-builtin
  return input.cholesky(name)

# The signature has to match with the one in python/op/array_ops.py,
# so we have k, padding_value, and align even though we don't use them here.
# pylint:disable=unused-argument
@dispatch.dispatch_for_types(linalg.diag_part, LinearOperator)
def _diag_part(
    input,  # pylint:disable=redefined-builtin
    name="diag_part",
    k=0,
    padding_value=0,
    align="RIGHT_LEFT"):
  return input.diag_part(name)
# pylint:enable=unused-argument

@dispatch.dispatch_for_types(linalg.det, LinearOperator)
def _det(input, name=None):  # pylint:disable=redefined-builtin
  return input.determinant(name)

@dispatch.dispatch_for_types(linalg.inv, LinearOperator)
def _inverse(input, adjoint=False, name=None):   # pylint:disable=redefined-builtin
  inv = input.inverse(name)
  if adjoint:
    inv = inv.adjoint()
  return inv

@dispatch.dispatch_for_types(linalg.logdet, LinearOperator)
def _logdet(matrix, name=None):
  if matrix.is_positive_definite and matrix.is_self_adjoint:
    return matrix.log_abs_determinant(name)
  raise ValueError("Expected matrix to be self-adjoint positive definite.")

@dispatch.dispatch_for_types(math_ops.matmul, LinearOperator)
def _matmul(  # pylint:disable=missing-docstring
    a,
    b,
    transpose_a=False,
    transpose_b=False,
    adjoint_a=False,
    adjoint_b=False,
    a_is_sparse=False,
    b_is_sparse=False,
    name=None):
  if transpose_a or transpose_b:
    raise ValueError("Transposing not supported at this time.")
  if a_is_sparse or b_is_sparse:
    raise ValueError("Sparse methods not supported at this time.")
  if not isinstance(a, LinearOperator):
    # We use the identity (B^HA^H)^H =  AB
    adjoint_matmul = b.matmul(
        a,
        adjoint=(not adjoint_b),
        adjoint_arg=(not adjoint_a),
        name=name)
    return linalg.adjoint(adjoint_matmul)
  return a.matmul(
      b, adjoint=adjoint_a, adjoint_arg=adjoint_b, name=name)

@dispatch.dispatch_for_types(linalg.solve, LinearOperator)
def _solve(
    matrix,
    rhs,
    adjoint=False,
    name=None):
  if not isinstance(matrix, LinearOperator):
    raise ValueError("Passing in `matrix` as a Tensor and `rhs` as a "
                     "LinearOperator is not supported.")
  return matrix.solve(rhs, adjoint=adjoint, name=name)

@dispatch.dispatch_for_types(linalg.trace, LinearOperator)
def _trace(x, name=None):
  return x.trace(name)