NAG: Optimize mathematical function using Nesterov Accelerated...

In PhilippScheller/visualDescend: Visulaization of descent method for multi-dimensional optimization functions

Description

This functions uses the Nesterov Accelerated Gradient (NAG) to find the minimum of a (multi-) dimensional mathematical function. The parameter 'phi' controls for the weight of prior gradients thus indirectly steering the velocity of the algorithm.

Usage

NAG(
  f,
  x0,
  max.iter = 100,
  step.size = 0.001,
  phi = 0.8,
  stop.grad = .Machine$double.eps
)

Arguments

f	a (multi-) dimensional function to be eptimized.
x0	the starting point of the optimization.
max.iter	the maximum number of iterations performed in the optimization.
step.size	the step size (sometimes referred to as 'learn-rate') of the optimization.
phi	controls the weight of the prior gradient contribution in the velocity.
stop.grad	the stop-criterion for the gradient change.

PhilippScheller/visualDescend documentation built on Feb. 5, 2020, 4:04 a.m.