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):
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):
# 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 = distribution_util.with_dependencies([aps], x)
return self._possibly_broadcast_batch_shape(x)
