diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r--[-rwxr-xr-x] | Wrappers/Python/ccpi/optimisation/algorithms/FBPD.py | 172 |
1 files changed, 86 insertions, 86 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/algorithms/FBPD.py b/Wrappers/Python/ccpi/optimisation/algorithms/FBPD.py index 322e9eb..798fb61 100755..100644 --- a/Wrappers/Python/ccpi/optimisation/algorithms/FBPD.py +++ b/Wrappers/Python/ccpi/optimisation/algorithms/FBPD.py @@ -1,86 +1,86 @@ -# -*- coding: utf-8 -*-
-# This work is part of the Core Imaging Library developed by
-# Visual Analytics and Imaging System Group of the Science Technology
-# Facilities Council, STFC
-
-# Copyright 2019 Edoardo Pasca
-
-# 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.
-"""
-Created on Thu Feb 21 11:09:03 2019
-
-@author: ofn77899
-"""
-
-from ccpi.optimisation.algorithms import Algorithm
-from ccpi.optimisation.funcs import ZeroFun
-
-class FBPD(Algorithm):
- '''FBPD Algorithm
-
- Parameters:
- x_init: initial guess
- f: constraint
- g: data fidelity
- h: regularizer
- opt: additional algorithm
- '''
- constraint = None
- data_fidelity = None
- regulariser = None
- def __init__(self, **kwargs):
- pass
- def set_up(self, x_init, operator=None, constraint=None, data_fidelity=None,\
- regulariser=None, opt=None):
-
- # default inputs
- if constraint is None:
- self.constraint = ZeroFun()
- else:
- self.constraint = constraint
- if data_fidelity is None:
- data_fidelity = ZeroFun()
- else:
- self.data_fidelity = data_fidelity
- if regulariser is None:
- self.regulariser = ZeroFun()
- else:
- self.regulariser = regulariser
-
- # algorithmic parameters
-
-
- # step-sizes
- self.tau = 2 / (self.data_fidelity.L + 2)
- self.sigma = (1/self.tau - self.data_fidelity.L/2) / self.regulariser.L
-
- self.inv_sigma = 1/self.sigma
-
- # initialization
- self.x = x_init
- self.y = operator.direct(self.x)
-
-
- def update(self):
-
- # primal forward-backward step
- x_old = self.x
- self.x = self.x - self.tau * ( self.data_fidelity.grad(self.x) + self.operator.adjoint(self.y) )
- self.x = self.constraint.prox(self.x, self.tau);
-
- # dual forward-backward step
- self.y = self.y + self.sigma * self.operator.direct(2*self.x - x_old);
- self.y = self.y - self.sigma * self.regulariser.prox(self.inv_sigma*self.y, self.inv_sigma);
-
- # time and criterion
- self.loss = self.constraint(self.x) + self.data_fidelity(self.x) + self.regulariser(self.operator.direct(self.x))
+# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2019 Edoardo Pasca + +# 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. +""" +Created on Thu Feb 21 11:09:03 2019 + +@author: ofn77899 +""" + +from ccpi.optimisation.algorithms import Algorithm +from ccpi.optimisation.funcs import ZeroFun + +class FBPD(Algorithm): + '''FBPD Algorithm + + Parameters: + x_init: initial guess + f: constraint + g: data fidelity + h: regularizer + opt: additional algorithm + ''' + constraint = None + data_fidelity = None + regulariser = None + def __init__(self, **kwargs): + pass + def set_up(self, x_init, operator=None, constraint=None, data_fidelity=None,\ + regulariser=None, opt=None): + + # default inputs + if constraint is None: + self.constraint = ZeroFun() + else: + self.constraint = constraint + if data_fidelity is None: + data_fidelity = ZeroFun() + else: + self.data_fidelity = data_fidelity + if regulariser is None: + self.regulariser = ZeroFun() + else: + self.regulariser = regulariser + + # algorithmic parameters + + + # step-sizes + self.tau = 2 / (self.data_fidelity.L + 2) + self.sigma = (1/self.tau - self.data_fidelity.L/2) / self.regulariser.L + + self.inv_sigma = 1/self.sigma + + # initialization + self.x = x_init + self.y = operator.direct(self.x) + + + def update(self): + + # primal forward-backward step + x_old = self.x + self.x = self.x - self.tau * ( self.data_fidelity.grad(self.x) + self.operator.adjoint(self.y) ) + self.x = self.constraint.prox(self.x, self.tau); + + # dual forward-backward step + self.y = self.y + self.sigma * self.operator.direct(2*self.x - x_old); + self.y = self.y - self.sigma * self.regulariser.prox(self.inv_sigma*self.y, self.inv_sigma); + + # time and criterion + self.loss = self.constraint(self.x) + self.data_fidelity(self.x) + self.regulariser(self.operator.direct(self.x)) |