gradDescent: Optimize mathematical function using gradient descent

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

Description

This functions uses the gradient descent algorithm to find the minimum of a (multi-) dimensional mathematical function.

Usage

gradDescent(
  f,
  x0,
  max.iter = 100,
  step.size = 0.001,
  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.
stop.grad	the stop-criterion for the gradient change.

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