def optimize(self,
num_vars,
objective_function,
gradient_function=None,
variable_bounds=None,
initial_point=None):
"""
Perform optimization.
Args:
num_vars (int) : number of parameters to be optimized.
objective_function (callable) : handle to a function that
computes the objective function.
gradient_function (callable) : handle to a function that
computes the gradient of the objective function, or
None if not available.
variable_bounds (list[(float, float)]) : deprecated
initial_point (numpy.ndarray[float]) : initial point.
Returns:
point, value, nfev
point: is a 1D numpy.ndarray[float] containing the solution
value: is a float with the objective function value
nfev: number of objective function calls made if available or None
"""
super().optimize(num_vars, objective_function, gradient_function,
variable_bounds, initial_point)
if initial_point is None:
initial_point = aqua_globals.random.rand(num_vars)
if gradient_function is None:
gradient_function = Optimizer.wrap_function(
Optimizer.gradient_num_diff, (objective_function, self._eps))
point, value, nfev = self.minimize(objective_function, initial_point,
gradient_function)
return point, value, nfev
def optimize(self, num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None):
super().optimize(num_vars, objective_function, gradient_function, variable_bounds, initial_point)
if gradient_function is None and self._batch_mode:
epsilon = self._options['eps']
gradient_function = Optimizer.wrap_function(Optimizer.gradient_num_diff, (objective_function, epsilon))
res = minimize(objective_function, initial_point, jac=gradient_function, tol=self._tol, method="CG", options=self._options)
return res.x, res.fun, res.nfev
def optimize(self, num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None):
super().optimize(num_vars, objective_function, gradient_function, variable_bounds, initial_point)
if gradient_function is None and self._max_evals_grouped > 1:
epsilon = self._options['eps']
gradient_function = Optimizer.wrap_function(Optimizer.gradient_num_diff, (objective_function, epsilon, self._max_evals_grouped))
res = minimize(objective_function, initial_point, jac=gradient_function, tol=self._tol,
bounds=variable_bounds, method="TNC", options=self._options)
# Note: nfev here seems to be iterations not function evaluations
return res.x, res.fun, res.nfev
def optimize(self, num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None):
super().optimize(num_vars, objective_function, gradient_function, variable_bounds, initial_point)
if gradient_function is None and self._max_evals_grouped > 1:
epsilon = self._options['epsilon']
gradient_function = Optimizer.wrap_function(Optimizer.gradient_num_diff, (objective_function, epsilon, self._max_evals_grouped))
approx_grad = True if gradient_function is None else False
sol, opt, info = sciopt.fmin_l_bfgs_b(objective_function, initial_point, bounds=variable_bounds,
fprime=gradient_function, approx_grad=approx_grad, **self._options)
return sol, opt, info['funcalls']
def optimize(
self,
num_vars: int,
objective_function: Callable[[np.ndarray], float],
gradient_function: Optional[Callable[[np.ndarray], float]] = None,
variable_bounds: Optional[List[Tuple[float, float]]] = None,
initial_point: Optional[np.ndarray] = None
) -> Tuple[np.ndarray, float, int]:
super().optimize(num_vars, objective_function, gradient_function,
variable_bounds, initial_point)
if initial_point is None:
initial_point = aqua_globals.random.random(num_vars)
if gradient_function is None:
gradient_function = Optimizer.wrap_function(
Optimizer.gradient_num_diff, (objective_function, self._eps))
point, value, nfev = self.minimize(objective_function, initial_point,
gradient_function)
return point, value, nfev, self.loss_list, self.params
def optimize(self,
num_vars,
objective_function,
gradient_function=None,
variable_bounds=None,
initial_point=None):
super().optimize(num_vars, objective_function, gradient_function,
variable_bounds, initial_point)
if gradient_function is None and self._max_evals_grouped > 1:
epsilon = self._options['eps']
gradient_function = Optimizer.wrap_function(
Optimizer.gradient_num_diff,
(objective_function, epsilon, self._max_evals_grouped))
intermediate_parameters = []
def callbackF(Xi):
intermediate_parameters.append(Xi)
res = minimize(objective_function,
initial_point,
jac=gradient_function,
tol=self._tol,
bounds=variable_bounds,
method="SLSQP",
options=self._options,
callback=callbackF)
intermediate_parameters = np.ravel(np.array(intermediate_parameters))
fichero_escribir = open('mis_fichero.txt', 'w')
for i in range(len(intermediate_parameters)):
fichero_escribir.write('%.10f\n' % intermediate_parameters[i])
fichero_escribir.close()
return res.x, res.fun, res.nfev
def optimizer(self, optimizer: Optimizer):
""" Sets optimizer """
super(VQE, self.__class__).optimizer.__set__(self,
optimizer) # type: ignore
if optimizer is not None:
optimizer.set_max_evals_grouped(self._max_evals_grouped)
def optimizer(self, optimizer: Optimizer):
""" Sets optimizer """
super().optimizer = optimizer
if optimizer is not None:
optimizer.set_max_evals_grouped(self._max_evals_grouped)
