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-rw-r--r--[-rwxr-xr-x]Wrappers/Python/ccpi/optimisation/algorithms/FBPD.py172
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))