def _matmul(self, x, adjoint=False, adjoint_arg=False):
if self._assert_proper_shapes:
x = linalg.adjoint(x) if adjoint_arg else x
aps = linear_operator_util.assert_compatible_matrix_dimensions(
self, x)
x = distribution_util.with_dependencies([aps], x)
if self.is_square:
# Note that adjoint has no effect since this matrix is self-adjoint.
if adjoint_arg:
output_shape = prefer_static.concat([
prefer_static.shape(x)[:-2],
[prefer_static.shape(x)[-1],
prefer_static.shape(x)[-2]]
],
axis=0)
else:
output_shape = prefer_static.shape(x)
return self._possibly_broadcast_batch_shape(
array_ops.zeros(shape=output_shape, dtype=x.dtype))
x_shape = prefer_static.shape(x)
n = self._num_columns if adjoint else self._num_rows
m = x_shape[-2] if adjoint_arg else x_shape[-1]
output_shape = prefer_static.concat([x_shape[:-2], [n, m]], axis=0)
zeros = array_ops.zeros(shape=output_shape, dtype=x.dtype)
return self._possibly_broadcast_batch_shape(zeros)
def _matmul(self, x, adjoint=False, adjoint_arg=False):
# Note that adjoint has no effect since this matrix is self-adjoint.
x = linalg.adjoint(x) if adjoint_arg else x
if self._assert_proper_shapes:
aps = linear_operator_util.assert_compatible_matrix_dimensions(self, x)
x = control_flow_ops.with_dependencies([aps], x)
return self._possibly_broadcast_batch_shape(x)
def _matmul(self, x, adjoint=False, adjoint_arg=False):
x = linalg.adjoint(x) if adjoint_arg else x
if self._assert_proper_shapes:
aps = linear_operator_util.assert_compatible_matrix_dimensions(
self, x)
x = distribution_util.with_dependencies([aps], x)
return x * self._make_multiplier_matrix(conjugate=adjoint)
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
if self._assert_proper_shapes:
aps = linear_operator_util.assert_compatible_matrix_dimensions(
self, rhs)
rhs = distribution_util.with_dependencies([aps], rhs)
return rhs / self._make_multiplier_matrix(conjugate=adjoint)
def _matmul(self, x, adjoint=False, adjoint_arg=False):
# Given a Toeplitz matrix, we can embed it in a Circulant matrix to perform
# efficient matrix multiplications. Given a Toeplitz matrix with first row
# [t_0, t_1, ... t_{n-1}] and first column [t0, t_{-1}, ..., t_{-(n-1)},
# let C by the circulant matrix with first column [t0, t_{-1}, ...,
# t_{-(n-1)}, 0, t_{n-1}, ..., t_1]. Also adjoin to our input vector `x`
# `n` zeros, to make it a vector of length `2n` (call it y). It can be shown
# that if we take the first n entries of `Cy`, this is equal to the Toeplitz
# multiplication. See:
# http://math.mit.edu/icg/resources/teaching/18.085-spring2015/toeplitz.pdf
# for more details.
x = linalg.adjoint(x) if adjoint_arg else x
expanded_x = array_ops.concat([x, array_ops.zeros_like(x)], axis=-2)
col = ops.convert_to_tensor(self.col)
row = ops.convert_to_tensor(self.row)
circulant_col = array_ops.concat([
col,
array_ops.zeros_like(col[..., 0:1]),
array_ops.reverse(row[..., 1:], axis=[-1])
],
axis=-1)
circulant = linear_operator_circulant.LinearOperatorCirculant(
fft_ops.fft(_to_complex(circulant_col)),
input_output_dtype=row.dtype)
result = circulant.matmul(expanded_x,
adjoint=adjoint,
adjoint_arg=False)
shape = self._shape_tensor(row=row, col=col)
return _ops.cast(
result[..., :self._domain_dimension_tensor(shape=shape), :],
self.dtype)
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 _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):
rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
spectrum = self._conj_spectrum if adjoint else self._spectrum_complex
rhs, spectrum = self._broadcast_batch_dims(rhs, spectrum)
rhs_vb = self._vectorize_then_blockify(rhs)
fft_rhs_vb = self._fft(rhs_vb)
solution_vb = self._ifft(fft_rhs_vb / spectrum)
x = self._unblockify_then_matricize(solution_vb)
return _ops.cast(x, self.dtype)
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
"""Default implementation of _solve."""
if self.is_square is False:
raise NotImplementedError(
"Solve is not yet implemented for non-square operators.")
logging.warn(
"Using (possibly slow) default implementation of solve."
" Requires conversion to a dense matrix and O(N^3) operations.")
rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
if self._can_use_cholesky():
return linear_operator_util.cholesky_solve_with_broadcast(
linalg_ops.cholesky(self.to_dense()), rhs)
return linear_operator_util.matrix_solve_with_broadcast(
self.to_dense(), rhs, adjoint=adjoint)
def _matmul(self, x, adjoint=False, adjoint_arg=False):
# Given a Toeplitz matrix, we can embed it in a Circulant matrix to perform
# efficient matrix multiplications. Given a Toeplitz matrix with first row
# [t_0, t_1, ... t_{n-1}] and first column [t0, t_{-1}, ..., t_{-(n-1)},
# let C by the circulant matrix with first column [t0, t_{-1}, ...,
# t_{-(n-1)}, 0, t_{n-1}, ..., t_1]. Also adjoin to our input vector `x`
# `n` zeros, to make it a vector of length `2n` (call it y). It can be shown
# that if we take the first n entries of `Cy`, this is equal to the Toeplitz
# multiplication. See:
# http://math.mit.edu/icg/resources/teaching/18.085-spring2015/toeplitz.pdf
# for more details.
x = linalg.adjoint(x) if adjoint_arg else x
expanded_x = array_ops.concat([x, array_ops.zeros_like(x)], axis=-2)
result = self._circulant.matmul(expanded_x,
adjoint=adjoint,
adjoint_arg=False)
return _ops.cast(result[..., :self.domain_dimension_tensor(), :],
self.dtype)
def _matmul(self, x, adjoint=False, adjoint_arg=False):
# Given a vector `v`, we would like to reflect `x` about the hyperplane
# orthogonal to `v` going through the origin. We first project `x` to `v`
# to get v * dot(v, x) / dot(v, v). After we project, we can reflect the
# projection about the hyperplane by flipping sign to get
# -v * dot(v, x) / dot(v, v). Finally, we can add back the component
# that is orthogonal to v. This is invariant under reflection, since the
# whole hyperplane is invariant. This component is equal to x - v * dot(v,
# x) / dot(v, v), giving the formula x - 2 * v * dot(v, x) / dot(v, v)
# for the reflection.
# Note that because this is a reflection, it lies in O(n) (for real vector
# spaces) or U(n) (for complex vector spaces), and thus is its own adjoint.
reflection_axis = ops.convert_to_tensor(self.reflection_axis)
x = linalg.adjoint(x) if adjoint_arg else x
normalized_axis = nn.l2_normalize(reflection_axis, axis=-1)
mat = normalized_axis[..., _ops.newaxis]
x_dot_normalized_v = _linalg.matmul(mat, x, adjoint_a=True)
return x - 2 * mat * x_dot_normalized_v
def _matmul(self, x, adjoint=False, adjoint_arg=False):
x = linalg.adjoint(x) if adjoint_arg else x
# With F the matrix of a DFT, and F^{-1}, F^H the inverse and Hermitian
# transpose, one can show that F^{-1} = F^{H} is the IDFT matrix. Therefore
# matmul(x) = F^{-1} diag(spectrum) F x,
# = F^{H} diag(spectrum) F x,
# so that
# matmul(x, adjoint=True) = F^{H} diag(conj(spectrum)) F x.
spectrum = self._conj_spectrum if adjoint else self._spectrum_complex
x = _ops.cast(x, spectrum.dtype)
x, spectrum = self._broadcast_batch_dims(x, spectrum)
x_vb = self._vectorize_then_blockify(x)
fft_x_vb = self._fft(x_vb)
block_vector_result = self._ifft(spectrum * fft_x_vb)
y = self._unblockify_then_matricize(block_vector_result)
return _ops.cast(y, self.dtype)
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)
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
# Here we follow the same use of Roth's column lemma as in `matmul`, with
# the key difference that we replace all `matmul` instances with `solve`.
# This follows from the property that inv(A x B) = inv(A) x inv(B).
# Below we document the shape manipulation for adjoint=False,
# adjoint_arg=False, but the general case of different adjoints is still
# handled.
if adjoint_arg:
rhs = linalg.adjoint(rhs)
# Always add a batch dimension to enable broadcasting to work.
batch_shape = self._compute_ones_matrix_shape()
rhs = rhs + array_ops.zeros(batch_shape, dtype=rhs.dtype)
# rhs has shape [B, R, C], where B represent some number of batch
# dimensions,
# R represents the number of rows, and C represents the number of columns.
# In order to apply Roth's column lemma, we need to operate on a batch of
# column vectors, so we reshape into a batch of column vectors. We put it
# at the front to ensure that broadcasting between operators to the batch
# dimensions B still works.
output = _rotate_last_dim(rhs, rotate_right=True)
# Also expand the shape to be [A, C, B, R]. The first dimension will be
# used to accumulate dimensions from each operator matmul.
output = output[_ops.newaxis, ...]
# In this loop, A is going to refer to the value of the accumulated
# dimension. A = 1 at the start, and will end up being self.range_dimension.
# V will refer to the last dimension. V = R at the start, and will end up
# being 1 in the end.
for operator in self.operators[:-1]:
# Reshape output from [A, C, B, V] to be
# [A, C, B, V / op.domain_dimension, op.domain_dimension]
if adjoint:
operator_dimension = operator.range_dimension_tensor()
else:
operator_dimension = operator.domain_dimension_tensor()
output = _unvec_by(output, operator_dimension)
# We are computing (XA^-1^T) = (A^-1 X^T)^T.
# output has [A, C, B, V / op.domain_dimension, op.domain_dimension],
# which is being converted to:
# [A, C, B, V / op.domain_dimension, op.range_dimension]
output = _linalg.matrix_transpose(output)
output = operator.solve(output, adjoint=adjoint, adjoint_arg=False)
output = _linalg.matrix_transpose(output)
# Rearrange it to [A * op.range_dimension, C, B, V / op.domain_dimension]
output = _rotate_last_dim(output, rotate_right=False)
output = _vec(output)
output = _rotate_last_dim(output, rotate_right=True)
# After the loop, we will have
# A = self.range_dimension / op[-1].range_dimension
# V = op[-1].domain_dimension
# We convert that using matvec to get:
# [A, C, B, op[-1].range_dimension]
output = self.operators[-1].solvevec(output, adjoint=adjoint)
# Rearrange shape to be [B1, ... Bn, self.range_dimension, C]
output = _rotate_last_dim(output, rotate_right=False)
output = _vec(output)
output = _rotate_last_dim(output, rotate_right=False)
if tensor_shape.TensorShape(rhs.shape).is_fully_defined():
column_dim = tensor_shape.TensorShape(rhs.shape)[-1]
broadcast_batch_shape = common_shapes.broadcast_shape(
tensor_shape.TensorShape(rhs.shape)[:-2], self.batch_shape)
if adjoint:
matrix_dimensions = [self.domain_dimension, column_dim]
else:
matrix_dimensions = [self.range_dimension, column_dim]
tensorshape_util.set_shape(
output, broadcast_batch_shape.concatenate(matrix_dimensions))
return output
def _matmul(self, x, adjoint=False, adjoint_arg=False):
# Here we heavily rely on Roth's column Lemma [1]:
# (A x B) * vec X = vec BXA^T,
# where vec stacks all the columns of the matrix under each other. In our
# case, x represents a batch of vec X (i.e. we think of x as a batch of
# column vectors, rather than a matrix). Each member of the batch can be
# reshaped to a matrix (hence we get a batch of matrices).
# We can iteratively apply this lemma by noting that if B is a Kronecker
# product, then we can apply the lemma again.
# [1] W. E. Roth, "On direct product matrices,"
# Bulletin of the American Mathematical Society, vol. 40, pp. 461-468,
# 1934
# Efficiency
# Naively doing the Kronecker product, by calculating the dense matrix and
# applying it will can take cubic time in the size of domain_dimension
# (assuming a square matrix). The other issue is that calculating the dense
# matrix can be prohibitively expensive, in that it can take a large amount
# of memory.
#
# This implementation avoids this memory blow up by only computing matmuls
# with the factors. In this way, we don't have to realize the dense matrix.
# In terms of complexity, if we have Kronecker Factors of size:
# (n1, n1), (n2, n2), (n3, n3), ... (nJ, nJ), with N = \prod n_i, and we
# have as input a [N, M] matrix, the naive approach would take O(N^2 M).
# With this approach (ignoring reshaping of tensors and transposes for now),
# the time complexity can be O(M * (\sum n_i) * N). There is also the
# benefit of batched multiplication (In this example, the batch size is
# roughly M * N) so this can be much faster. However, not factored in are
# the costs of the several transposing of tensors, which can affect cache
# behavior.
# Below we document the shape manipulation for adjoint=False,
# adjoint_arg=False, but the general case of different adjoints is still
# handled.
if adjoint_arg:
x = linalg.adjoint(x)
# Always add a batch dimension to enable broadcasting to work.
batch_shape = self._compute_ones_matrix_shape()
x = x + array_ops.zeros(batch_shape, dtype=x.dtype)
# x has shape [B, R, C], where B represent some number of batch dimensions,
# R represents the number of rows, and C represents the number of columns.
# In order to apply Roth's column lemma, we need to operate on a batch of
# column vectors, so we reshape into a batch of column vectors. We put it
# at the front to ensure that broadcasting between operators to the batch
# dimensions B still works.
output = _rotate_last_dim(x, rotate_right=True)
# Also expand the shape to be [A, C, B, R]. The first dimension will be
# used to accumulate dimensions from each operator matmul.
output = output[_ops.newaxis, ...]
# In this loop, A is going to refer to the value of the accumulated
# dimension. A = 1 at the start, and will end up being self.range_dimension.
# V will refer to the last dimension. V = R at the start, and will end up
# being 1 in the end.
for operator in self.operators[:-1]:
# Reshape output from [A, C, B, V] to be
# [A, C, B, V / op.domain_dimension, op.domain_dimension]
if adjoint:
operator_dimension = operator.range_dimension_tensor()
else:
operator_dimension = operator.domain_dimension_tensor()
output = _unvec_by(output, operator_dimension)
# We are computing (XA^T) = (AX^T)^T.
# output has [A, C, B, V / op.domain_dimension, op.domain_dimension],
# which is being converted to:
# [A, C, B, V / op.domain_dimension, op.range_dimension]
output = _linalg.matrix_transpose(output)
output = operator.matmul(output,
adjoint=adjoint,
adjoint_arg=False)
output = _linalg.matrix_transpose(output)
# Rearrange it to [A * op.range_dimension, C, B, V / op.domain_dimension]
output = _rotate_last_dim(output, rotate_right=False)
output = _vec(output)
output = _rotate_last_dim(output, rotate_right=True)
# After the loop, we will have
# A = self.range_dimension / op[-1].range_dimension
# V = op[-1].domain_dimension
# We convert that using matvec to get:
# [A, C, B, op[-1].range_dimension]
output = self.operators[-1].matvec(output, adjoint=adjoint)
# Rearrange shape to be [B1, ... Bn, self.range_dimension, C]
output = _rotate_last_dim(output, rotate_right=False)
output = _vec(output)
output = _rotate_last_dim(output, rotate_right=False)
if tensor_shape.TensorShape(x.shape).is_fully_defined():
column_dim = tensor_shape.TensorShape(x.shape)[-1]
broadcast_batch_shape = common_shapes.broadcast_shape(
tensor_shape.TensorShape(x.shape)[:-2], self.batch_shape)
if adjoint:
matrix_dimensions = [self.domain_dimension, column_dim]
else:
matrix_dimensions = [self.range_dimension, column_dim]
tensorshape_util.set_shape(
output, broadcast_batch_shape.concatenate(matrix_dimensions))
return output
def _reshape_for_efficiency(a,
b,
transpose_a=False,
transpose_b=False,
adjoint_a=False,
adjoint_b=False):
"""Maybe reshape a, b, and return an inverse map. For matmul/solve."""
def identity(x):
return x
# At this point, we have not taken transpose/adjoint of a/b.
still_need_to_transpose = True
if _ops.TensorShape(a.shape).ndims is None or _ops.TensorShape(
b.shape).ndims is None:
return a, b, identity, still_need_to_transpose
# This could be handled in the future, but seems less common.
if _ops.TensorShape(a.shape).ndims >= _ops.TensorShape(b.shape).ndims:
return a, b, identity, still_need_to_transpose
# From now on, we might modify b, but will not modify a.
# Suppose:
# _ops.TensorShape(a.shape) = C + [m, n], _ops.TensorShape(b.shape) =
# _ops.TensorShape(b.shape) = S + C + [n, r]
b_extra_ndims = _ops.TensorShape(b.shape).ndims - _ops.TensorShape(
a.shape).ndims
# b_extra_sh = S, b_main_sh = C + [n, r]
b_extra_sh = array_ops.shape(b)[:b_extra_ndims]
b_main_sh = array_ops.shape(b)[b_extra_ndims:]
# No reason to flip unless the extra dims of b are big enough. Why?
# Assume adjoint/transpose = False. Then...
# By not flipping, we have to replicate a to shape
# b_extra_sh + _ops.TensorShape(a.shape),
# which could use extra memory. But in all cases, the final output has shape
# b_extra_sh + _ops.TensorShape(a.shape)[:-1] + _ops.TensorShape([b.shape)[-1]]
# So we only end up creating a larger object if the end dim of b is smaller
# than the end dim of a. This often happens, e.g. if b was a vector that was
# expanded to a matrix (by appending a singleton).
# Since adjoint/transpose may not be False, we must make adjustments here.
# The dim of b that holds the multiple equations.
a_domain_sz_ = _ops.TensorShape(
a.shape)[-2 if adjoint_a or transpose_a else -1]
b_eq_sz_ = _ops.TensorShape(
b.shape)[-2 if adjoint_b or transpose_b else -1]
b_extra_sz_ = (
np.prod(_ops.TensorShape(b.shape)[:b_extra_ndims].as_list()) if
_ops.TensorShape(b.shape)[:b_extra_ndims].is_fully_defined() else None)
if (a_domain_sz_ is not None and b_eq_sz_ is not None
and b_extra_sz_ is not None):
if b_extra_sz_ < 2 or a_domain_sz_ <= b_eq_sz_:
return a, b, identity, still_need_to_transpose
# At this point, we're flipping for sure!
# Any transposes/adjoints will happen here explicitly, rather than in calling
# code. Why? To avoid having to write separate complex code for each case.
if adjoint_a:
a = linalg.adjoint(a)
elif transpose_a:
a = linalg.transpose(a)
if adjoint_b:
b = linalg.adjoint(b)
elif transpose_b:
b = linalg.transpose(b)
still_need_to_transpose = False
# Recompute shapes, since the transpose/adjoint may have changed them.
b_extra_sh = array_ops.shape(b)[:b_extra_ndims]
b_main_sh = array_ops.shape(b)[b_extra_ndims:]
# Permutation to put the extra dims at the end.
perm = (np.concatenate(
(np.arange(b_extra_ndims,
_ops.TensorShape(b.shape).ndims), np.arange(
0, b_extra_ndims)), 0))
b_extra_on_end = array_ops.transpose(b, perm=perm)
# Now squash this end into one long dim.
b_squashed_end = array_ops.reshape(
b_extra_on_end, array_ops.concat((b_main_sh[:-1], [-1]), 0))
def reshape_inv(y):
# Expand the extra dims hanging off the end, "b_extra_sh".
# Note we use y_sh[:-1] + [b_main_sh[-1]] rather than b_main_sh, because y
# Could have different batch dims than a and b, because of broadcasting.
y_extra_shape = array_ops.concat(
(array_ops.shape(y)[:-1], [b_main_sh[-1]], b_extra_sh), 0)
y_extra_on_end = array_ops.reshape(y, y_extra_shape)
inverse_perm = np.argsort(perm)
return array_ops.transpose(y_extra_on_end, perm=inverse_perm)
return a, b_squashed_end, reshape_inv, still_need_to_transpose
def _matmul(self, x, adjoint=False, adjoint_arg=False):
diag_term = math_ops.conj(self._diag) if adjoint else self._diag
x = linalg.adjoint(x) if adjoint_arg else x
diag_mat = array_ops.expand_dims(diag_term, -1)
return diag_mat * x
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
diag_term = math_ops.conj(self._diag) if adjoint else self._diag
rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
inv_diag_mat = array_ops.expand_dims(1. / diag_term, -1)
return rhs * inv_diag_mat
def _to_dense(self):
if self.is_self_adjoint:
return self.operator.to_dense()
return linalg.adjoint(self.operator.to_dense())
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.
Given the blockwise `n + 1`-by-`n + 1` linear operator:
op = [[A_00 0 ... 0 ... 0],
[A_10 A_11 ... 0 ... 0],
...
[A_k0 A_k1 ... A_kk ... 0],
...
[A_n0 A_n1 ... A_nk ... A_nn]]
we find `x = op.solve(y)` by observing that
`y_k = A_k0.matmul(x_0) + A_k1.matmul(x_1) + ... + A_kk.matmul(x_k)`
and therefore
`x_k = A_kk.solve(y_k -
A_k0.matmul(x_0) - ... - A_k(k-1).matmul(x_(k-1)))`
where `x_k` and `y_k` are the `k`th blocks obtained by decomposing `x`
and `y` along their appropriate axes.
We first solve `x_0 = A_00.solve(y_0)`. Proceeding inductively, we solve
for `x_k`, `k = 1..n`, given `x_0..x_(k-1)`.
The adjoint case is solved similarly, beginning with
`x_n = A_nn.solve(y_n, adjoint=True)` and proceeding backwards.
Examples:
```python
# Make an operator acting like batch matrix A. Assume tensor_shape.TensorShape(A.shape) = [..., M, N]
operator = LinearOperator(...)
tensor_shape.TensorShape(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,
or a list of `Tensor`s. `Tensor`s are treated like a [batch] matrices
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, linear_operator.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):
block_dimensions = (self._block_domain_dimensions()
if adjoint else self._block_range_dimensions())
arg_dim = -1 if adjoint_arg else -2
blockwise_arg = linear_operator_util.arg_is_blockwise(
block_dimensions, rhs, arg_dim)
if blockwise_arg:
for i, block in enumerate(rhs):
if not isinstance(block, linear_operator.LinearOperator):
block = ops.convert_to_tensor(block)
# self._check_input_dtype(block)
block_dimensions[i].assert_is_compatible_with(
tensor_shape.TensorShape(block.shape)[arg_dim])
rhs[i] = block
if adjoint_arg:
split_rhs = [linalg.adjoint(y) for y in rhs]
else:
split_rhs = rhs
else:
rhs = ops.convert_to_tensor(rhs, name="rhs")
# self._check_input_dtype(rhs)
op_dimension = (self.domain_dimension
if adjoint else self.range_dimension)
op_dimension.assert_is_compatible_with(
tensor_shape.TensorShape(rhs.shape)[arg_dim])
rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
split_rhs = linear_operator_util.split_arg_into_blocks(
self._block_domain_dimensions(),
self._block_domain_dimension_tensors,
rhs,
axis=-2)
solution_list = []
if adjoint:
# For an adjoint blockwise lower-triangular linear operator, the system
# must be solved bottom to top. Iterate backwards over rows of the
# adjoint (i.e. columns of the non-adjoint operator).
for index in reversed(range(len(self.operators))):
y = split_rhs[index]
# Iterate top to bottom over the operators in the off-diagonal portion
# of the column-partition (i.e. row-partition of the adjoint), apply
# the operator to the respective block of the solution found in
# previous iterations, and subtract the result from the `rhs` block.
# For example,let `A`, `B`, and `D` be the linear operators in the top
# row-partition of the adjoint of
# `LinearOperatorBlockLowerTriangular([[A], [B, C], [D, E, F]])`,
# and `x_1` and `x_2` be blocks of the solution found in previous
# iterations of the outer loop. The following loop (when `index == 0`)
# expresses
# `Ax_0 + Bx_1 + Dx_2 = y_0` as `Ax_0 = y_0*`, where
# `y_0* = y_0 - Bx_1 - Dx_2`.
for j in reversed(range(index + 1, len(self.operators))):
y -= self.operators[j][index].matmul(
solution_list[len(self.operators) - 1 - j],
adjoint=adjoint)
# Continuing the example above, solve `Ax_0 = y_0*` for `x_0`.
solution_list.append(self._diagonal_operators[index].solve(
y, adjoint=adjoint))
solution_list.reverse()
else:
# Iterate top to bottom over the row-partitions.
for row, y in zip(self.operators, split_rhs):
# Iterate left to right over the operators in the off-diagonal portion
# of the row-partition, apply the operator to the block of the
# solution found in previous iterations, and subtract the result from
# the `rhs` block. For example, let `D`, `E`, and `F` be the linear
# operators in the bottom row-partition of
# `LinearOperatorBlockLowerTriangular([[A], [B, C], [D, E, F]])` and
# `x_0` and `x_1` be blocks of the solution found in previous
# iterations of the outer loop. The following loop
# (when `index == 2`), expresses
# `Dx_0 + Ex_1 + Fx_2 = y_2` as `Fx_2 = y_2*`, where
# `y_2* = y_2 - D_x0 - Ex_1`.
for i, operator in enumerate(row[:-1]):
y -= operator.matmul(solution_list[i], adjoint=adjoint)
# Continuing the example above, solve `Fx_2 = y_2*` for `x_2`.
solution_list.append(row[-1].solve(y, adjoint=adjoint))
if blockwise_arg:
return solution_list
solution_list = linear_operator_util.broadcast_matrix_batch_dims(
solution_list)
return array_ops.concat(solution_list, axis=-2)
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
return linalg.triangular_solve(
self._get_tril(), rhs, lower=True, adjoint=adjoint)
