Source code for pysit.optimization.gradient_descent
import time
import copy
import sys
import numpy as np
from pysit.optimization.optimization import OptimizationBase
__all__=['GradientDescent']
__docformat__ = "restructuredtext en"
[docs]class GradientDescent(OptimizationBase):
def __init__(self, objective, *args, **kwargs):
OptimizationBase.__init__(self, objective, *args, **kwargs)
self.prev_alpha = None
def _select_step(self, shots, current_objective_value, gradient, iteration, objective_arguments, **kwargs):
"""Compute the adjustment of the gradient step for a set of shots.
Gives the step s as a scalar multiple of the gradient vector.
Parameters
----------
shots : list of pysit.Shot
List of Shots for which to compute.
grad : ndarray
Gradient vector.
i : int
Current time index.
"""
# search the negative gradient
direction = -1*gradient
alpha0_kwargs = {'reset' : False}
if iteration == 0:
alpha0_kwargs = {'reset' : True}
alpha = self.select_alpha(shots, gradient, direction, objective_arguments,
current_objective_value=current_objective_value,
alpha0_kwargs=alpha0_kwargs, **kwargs)
self._print(' alpha {0}'.format(alpha))
self.store_history('alpha', iteration, alpha)
step = alpha * direction
return step
def _compute_alpha0(self, phi0, grad0, reset=False, upscale_factor=None, **kwargs):
if reset or (self.prev_alpha is None):
return phi0 / (grad0.norm()*np.prod(self.solver.mesh.deltas))**2
else:
return self.prev_alpha / upscale_factor