Implementation of a Parametrised Reduced Functional #241
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…educedFunctional` where parameters can be updated but are not included in the derivative calculations: 1. Adds a `parameter_update` method 2. Parameters are appended at the end of the list of optimization controls, so `derivative_components` is not a required argument. 3. The `derivative` method returns only derivative corresponding to optimization controls.
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In the current implementation prf = ParameterisedReducedFunctional(functional, user_controls, parameters)
assert len(prf.controls) == len(user_controls) + len(parameters)This will also be a problem later because the optimisers will expect: len(prf.derivative()) == len(prf.controls)when you will actually have (correctly): len(prf.derivative()) == len(prf.user_controls)If this is the case then you may need to override the To get around this, you may have to instead inherit from the |
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Current method to use parameters
The
derivative_componentsoptional argument ofReducedFunctionalis used after adding parameters to the list of controls, to specify which components are to be zeroed out (by omitting them fromderivative_components). This allows the user to update parameters by calling the Reduced Functional while zeroing out the gradient with respect to the parameters.Parametrised Reduced Functional
ParametrisedReducedFunctionalis a subclass ofReducedFunctionalwith wrappingcallandderivativemethods, with the parameters as attributes. Theparameter_updatemethod is called to update parameters. The parameters are not included in the derivative calculation and the optional argumentderivative_componentsis not required.