Source code for pennylane.optimize.momentum

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[docs]class MomentumOptimizer(GradientDescentOptimizer): r"""Gradient-descent optimizer with momentum. The momentum optimizer adds a "momentum" term to gradient descent which considers the past gradients: .. math:: x^{(t+1)} = x^{(t)} - a^{(t+1)}. The accumulator term :math:a is updated as follows: .. math:: a^{(t+1)} = m a^{(t)} + \eta \nabla f(x^{(t)}), with user defined parameters: * :math:\eta: the step size * :math:m: the momentum Args: stepsize (float): user-defined hyperparameter :math:\eta momentum (float): user-defined hyperparameter :math:m """ def __init__(self, stepsize=0.01, momentum=0.9): super().__init__(stepsize) self.momentum = momentum self.accumulation = None
[docs] def apply_grad(self, grad, x): r"""Update the variables x to take a single optimization step. Flattens and unflattens the inputs to maintain nested iterables as the parameters of the optimization. Args: grad (array): The gradient of the objective function at point :math:x^{(t)}: :math:\nabla f(x^{(t)}) x (array): the current value of the variables :math:x^{(t)} Returns: array: the new values :math:x^{(t+1)} """ grad_flat = _flatten(grad) x_flat = _flatten(x) if self.accumulation is None: self.accumulation = [self._stepsize * g for g in grad_flat] else: self.accumulation = [self.momentum * a + self._stepsize * g for a, g in zip(self.accumulation, grad_flat)] x_new_flat = [e-a for a, e in zip(self.accumulation, x_flat)] return unflatten(x_new_flat, x)