Gradient descent (fixed learning rate)

class pints.GradientDescent(x0, sigma0=0.1, boundaries=None)

Gradient-descent method with a fixed learning rate.

ask()

See Optimiser.ask().

fbest()

See Optimiser.fbest().

learning_rate()

Returns this optimiser’s learning rate.

n_hyper_parameters()

See pints.TunableMethod.n_hyper_parameters().

name()

See Optimiser.name().

needs_sensitivities()

See Optimiser.needs_sensitivities().

running()

See Optimiser.running().

set_hyper_parameters(x)

See pints.TunableMethod.set_hyper_parameters().

The hyper-parameter vector is [learning_rate].

set_learning_rate(eta)

Sets the learning rate for this optimiser.

Parameters:eta (float) – The learning rate, as a float greater than zero.

stop()

Checks if this method has run into trouble and should terminate. Returns False if everything’s fine, or a short message (e.g. “Ill-conditioned matrix.”) if the method should terminate.

tell(reply)

See Optimiser.tell().

xbest()

See Optimiser.xbest().