# Copyright 2019 United Kingdom Research and Innovation
# Copyright 2019 The University of Manchester
#
# 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.
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# Authors:
# CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
from cil.optimisation.functions import Function
from cil.framework import BlockDataContainer
from numbers import Number
[docs]class BlockFunction(Function):
r""" BlockFunction represents a *separable sum* function :math:`F` defined as
.. math:: F:X_{1}\times X_{2}\cdots\times X_{m} \rightarrow (-\infty, \infty]
where :math:`F` is the separable sum of functions :math:`(f_{i})_{i=1}^{m}`,
.. math:: F(x_{1}, x_{2}, \cdots, x_{m}) = \overset{m}{\underset{i=1}{\sum}}f_{i}(x_{i}), \mbox{ with } f_{i}: X_{i} \rightarrow (-\infty, \infty].
A nice property (due to it's separability structure) is that the proximal operator
can be decomposed along the proximal operators of each function :math:`f_{i}`.
.. math:: \mathrm{prox}_{\tau F}(x) = ( \mathrm{prox}_{\tau f_{i}}(x_{i}) )_{i=1}^{m}
In addition, if :math:`\tau := (\tau_{1},\dots,\tau_{m})`, then
.. math:: \mathrm{prox}_{\tau F}(x) = ( \mathrm{prox}_{\tau_{i} f_{i}}(x_{i}) )_{i=1}^{m}
"""
[docs] def __init__(self, *functions):
super(BlockFunction, self).__init__()
self.functions = functions
self.length = len(self.functions)
@property
def L(self):
# compute Lipschitz constant if possible
tmp_L = 0
for func in self.functions:
if func.L is not None:
tmp_L += func.L
else:
tmp_L = None
break
return tmp_L
[docs] def __call__(self, x):
r""" Returns the value of the BlockFunction :math:`F`
.. math:: F(x) = \overset{m}{\underset{i=1}{\sum}}f_{i}(x_{i}), \mbox{ where } x = (x_{1}, x_{2}, \cdots, x_{m}), \quad i = 1,2,\dots,m
Parameter:
x : BlockDataContainer and must have as many rows as self.length
returns ..math:: \sum(f_i(x_i))
"""
if self.length != x.shape[0]:
raise ValueError('BlockFunction and BlockDataContainer have incompatible size')
t = 0
for i in range(x.shape[0]):
t += self.functions[i](x.get_item(i))
return t
[docs] def convex_conjugate(self, x):
r"""Returns the value of the convex conjugate of the BlockFunction at :math:`x^{*}`.
.. math:: F^{*}(x^{*}) = \overset{m}{\underset{i=1}{\sum}}f_{i}^{*}(x^{*}_{i})
Parameter:
x : BlockDataContainer and must have as many rows as self.length
"""
if self.length != x.shape[0]:
raise ValueError('BlockFunction and BlockDataContainer have incompatible size')
t = 0
for i in range(x.shape[0]):
t += self.functions[i].convex_conjugate(x.get_item(i))
return t
[docs] def proximal(self, x, tau, out = None):
r"""Proximal operator of the BlockFunction at x:
.. math:: \mathrm{prox}_{\tau F}(x) = (\mathrm{prox}_{\tau f_{i}}(x_{i}))_{i=1}^{m}
Parameter:
x : BlockDataContainer and must have as many rows as self.length
"""
if self.length != x.shape[0]:
raise ValueError('BlockFunction and BlockDataContainer have incompatible size')
if out is None:
out = [None]*self.length
if isinstance(tau, Number):
for i in range(self.length):
out[i] = self.functions[i].proximal(x.get_item(i), tau)
else:
for i in range(self.length):
out[i] = self.functions[i].proximal(x.get_item(i), tau.get_item(i))
return BlockDataContainer(*out)
else:
if isinstance(tau, Number):
for i in range(self.length):
self.functions[i].proximal(x.get_item(i), tau, out[i])
else:
for i in range(self.length):
self.functions[i].proximal(x.get_item(i), tau.get_item(i), out[i])
[docs] def gradient(self, x, out=None):
r"""Returns the value of the gradient of the BlockFunction function at x.
.. math:: F'(x) = [f_{1}'(x_{1}), ... , f_{m}'(x_{m})]
Parameter:
x : BlockDataContainer and must have as many rows as self.length
"""
if self.length != x.shape[0]:
raise ValueError('BlockFunction and BlockDataContainer have incompatible size')
out = [None]*self.length
for i in range(self.length):
out[i] = self.functions[i].gradient(x.get_item(i))
return BlockDataContainer(*out)
[docs] def proximal_conjugate(self, x, tau, out = None):
r"""Proximal operator of the convex conjugate of BlockFunction at x:
.. math:: \mathrm{prox}_{\tau F^{*}}(x) = (\mathrm{prox}_{\tau f^{*}_{i}}(x^{*}_{i}))_{i=1}^{m}
Parameter:
x : BlockDataContainer and must have as many rows as self.length
"""
if self.length != x.shape[0]:
raise ValueError('BlockFunction and BlockDataContainer have incompatible size')
if out is not None:
if isinstance(tau, Number):
for i in range(self.length):
self.functions[i].proximal_conjugate(x.get_item(i), tau, out=out.get_item(i))
else:
for i in range(self.length):
self.functions[i].proximal_conjugate(x.get_item(i), tau.get_item(i),out=out.get_item(i))
else:
out = [None]*self.length
if isinstance(tau, Number):
for i in range(self.length):
out[i] = self.functions[i].proximal_conjugate(x.get_item(i), tau)
else:
for i in range(self.length):
out[i] = self.functions[i].proximal_conjugate(x.get_item(i), tau.get_item(i))
return BlockDataContainer(*out)
def __getitem__(self, row):
return self.functions[row]
[docs] def __rmul__(self, other):
'''Define multiplication with a scalar
:param other: number
Returns a new `BlockFunction`_ containing the product of the scalar with all the functions in the block
'''
if not isinstance(other, Number):
raise NotImplemented
return BlockFunction( * [ other * el for el in self.functions] )