STT-tensorflow/tensorflow/python/ops/linalg/cholesky_registrations.py
A. Unique TensorFlower f1e2b518e1 Add cholesky factorization to LinearOperator.
PiperOrigin-RevId: 221549742
2018-11-14 18:20:22 -08:00

102 lines
4.0 KiB
Python

# Copyright 2018 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.
# ==============================================================================
"""Registrations for LinearOperator.cholesky."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.linalg import linear_operator
from tensorflow.python.ops.linalg import linear_operator_algebra
from tensorflow.python.ops.linalg import linear_operator_block_diag
from tensorflow.python.ops.linalg import linear_operator_diag
from tensorflow.python.ops.linalg import linear_operator_identity
from tensorflow.python.ops.linalg import linear_operator_kronecker
from tensorflow.python.ops.linalg import linear_operator_lower_triangular
# By default, compute the Cholesky of the dense matrix, and return a
# LowerTriangular operator. Methods below specialize this registration.
@linear_operator_algebra.RegisterCholesky(linear_operator.LinearOperator)
def _cholesky_linear_operator(linop):
return linear_operator_lower_triangular.LinearOperatorLowerTriangular(
linalg_ops.cholesky(linop.to_dense()),
is_non_singular=True,
is_self_adjoint=False,
is_square=True)
@linear_operator_algebra.RegisterCholesky(
linear_operator_diag.LinearOperatorDiag)
def _cholesky_diag(diag_operator):
return linear_operator_diag.LinearOperatorDiag(
math_ops.sqrt(diag_operator.diag),
is_non_singular=True,
is_self_adjoint=True,
is_positive_definite=True,
is_square=True)
@linear_operator_algebra.RegisterCholesky(
linear_operator_identity.LinearOperatorIdentity)
def _cholesky_identity(identity_operator):
return linear_operator_identity.LinearOperatorIdentity(
num_rows=identity_operator._num_rows, # pylint: disable=protected-access
batch_shape=identity_operator.batch_shape,
dtype=identity_operator.dtype,
is_non_singular=True,
is_self_adjoint=True,
is_positive_definite=True,
is_square=True)
@linear_operator_algebra.RegisterCholesky(
linear_operator_identity.LinearOperatorScaledIdentity)
def _cholesky_scaled_identity(identity_operator):
return linear_operator_identity.LinearOperatorScaledIdentity(
num_rows=identity_operator._num_rows, # pylint: disable=protected-access
multiplier=math_ops.sqrt(identity_operator.multiplier),
is_non_singular=True,
is_self_adjoint=True,
is_positive_definite=True,
is_square=True)
@linear_operator_algebra.RegisterCholesky(
linear_operator_block_diag.LinearOperatorBlockDiag)
def _cholesky_block_diag(block_diag_operator):
# We take the cholesky of each block on the diagonal.
return linear_operator_block_diag.LinearOperatorBlockDiag(
operators=[
operator.cholesky() for operator in block_diag_operator.operators],
is_non_singular=True,
is_self_adjoint=False,
is_square=True)
@linear_operator_algebra.RegisterCholesky(
linear_operator_kronecker.LinearOperatorKronecker)
def _cholesky_kronecker(kronecker_operator):
# Cholesky decomposition of a Kronecker product is the Kronecker product
# of cholesky decompositions.
return linear_operator_kronecker.LinearOperatorKronecker(
operators=[
operator.cholesky() for operator in kronecker_operator.operators],
is_non_singular=True,
is_self_adjoint=False,
is_square=True)