diff options
| -rw-r--r-- | Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py | 23 |
1 files changed, 17 insertions, 6 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py b/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py index 0f3defe..3ac4358 100644 --- a/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py +++ b/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py @@ -9,16 +9,20 @@ Created on Fri Mar 8 09:55:36 2019 import numpy as np #from ccpi.optimisation.funcs import Function from ccpi.optimisation.functions import Function +from ccpi.optimisation.functions import ScaledFunction class FunctionOperatorComposition(Function): def __init__(self, operator, function): - + super(FunctionOperatorComposition, self).__init__() self.function = function self.operator = operator - self.L = 2*self.function.alpha*operator.norm()**2 - super(FunctionOperatorComposition, self).__init__() + alpha = 1 + if isinstance (function, ScaledFunction): + alpha = function.scalar + self.L = 2 * alpha * operator.norm()**2 + def __call__(self, x): @@ -45,10 +49,17 @@ class FunctionOperatorComposition(Function): return self.function.proximal_conjugate(x, tau) - def gradient(self, x): + def gradient(self, x, out=None): ''' Gradient takes into account the Operator''' - - return self.operator.adjoint(self.function.gradient(self.operator.direct(x))) + if out is None: + return self.operator.adjoint( + self.function.gradient(self.operator.direct(x)) + ) + else: + self.operator.adjoint( + self.function.gradient(self.operator.direct(x), + out=out) + )
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