def __init__(self, functional, controls, solver):
self.solver = solver
if not isinstance(solver, Solver):
raise ValueError, "solver argument of wrong type."
self._functional = functional
if not isinstance(functional, PrototypeFunctional):
raise ValueError, "invalid functional argument."
# Hidden attributes
self._solver_params = solver.parameters
self._problem_params = solver.problem.parameters
self._time_integrator = None
# Controls
self.controls = enlisting.enlist(controls)
# Conform to dolfin-adjoint API
self.scale = 1
self.eval_cb_pre = lambda *args: None
self.eval_cb_post = lambda *args: None
self.derivative_cb_pre = lambda *args: None
self.derivative_cb_post = lambda *args: None
self.replay_cb = lambda *args: None
self.hessian_cb = lambda *args: None
self.cache = None
self.current_func_value = None
self.hessian = None
self.functional = Functional(None)
def __init__(self, functional, controls, solver):
self.solver = solver
if not isinstance(solver, Solver):
raise ValueError, "solver argument of wrong type."
self._functional = functional
if not isinstance(functional, PrototypeFunctional):
raise ValueError, "invalid functional argument."
# Hidden attributes
self._solver_params = solver.parameters
self._problem_params = solver.problem.parameters
self._time_integrator = None
# Controls
self.controls = enlisting.enlist(controls)
# Conform to dolfin-adjoint API
self.scale = 1
self.eval_cb_pre = lambda *args: None
self.eval_cb_post = lambda *args: None
self.derivative_cb_pre = lambda *args: None
self.derivative_cb_post = lambda *args: None
self.replay_cb = lambda *args: None
self.hessian_cb = lambda *args: None
self.cache = None
self.current_func_value = None
self.hessian = None
self.functional = Functional(None)
def derivative(self, forget=False, **kwargs):
""" Computes the first derivative of the functional with respect to its
controls by solving the adjoint equations. """
log(INFO, 'Start evaluation of dj')
timer = Timer("dj evaluation")
self.functional = self.time_integrator.dolfin_adjoint_functional(self.solver.state)
dj = compute_gradient(self.functional, self.controls, forget=forget, **kwargs)
parameters["adjoint"]["stop_annotating"] = False
log(INFO, "Runtime: " + str(timer.stop()) + " s")
return enlisting.enlist(dj)
def derivative(self, forget=False, **kwargs):
""" Computes the first derivative of the functional with respect to its
controls by solving the adjoint equations. """
log(INFO, 'Start evaluation of dj')
timer = Timer("dj evaluation")
if not hasattr(self, "time_integrator"):
self.evaluate()
self.functional = self.time_integrator.dolfin_adjoint_functional(
self.solver.state)
dj = compute_gradient(self.functional,
self.controls,
forget=forget,
**kwargs)
parameters["adjoint"]["stop_annotating"] = False
log(INFO, "Runtime: " + str(timer.stop()) + " s")
return enlisting.enlist(dj)
def __call__(self, value):
""" Evaluates the reduced functional for the given control value.
Args:
value: The point in control space where to perform the Taylor test. Must be of the same type as the Control (e.g. Function, Constant or lists of latter).
Returns:
float: The functional value.
"""
value = enlisting.enlist(value)
# Update the control values.
# Note that we do not update the control values on the tape,
# because OpenTidalFarm reannotates the tape in each iteration.
for c, v in zip(self.controls, value):
vec = c.coeff.vector()
if vec.id() == v.vector().id():
continue
vec.zero()
vec.axpy(1, v.vector())
return self.evaluate()
def __call__(self, value):
""" Evaluates the reduced functional for the given control value.
Args:
value: The point in control space where to perform the Taylor test. Must be of the same type as the Control (e.g. Function, Constant or lists of latter).
Returns:
float: The functional value.
"""
value = enlisting.enlist(value)
# Update the control values.
# Note that we do not update the control values on the tape,
# because OpenTidalFarm reannotates the tape in each iteration.
for c, v in zip(self.controls, value):
vec = c.coeff.vector()
if vec.id() == v.vector().id():
continue
vec.zero()
vec.axpy(1, v.vector())
return self.evaluate()
