R/optim.RMSprop.R

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

#' Optimize mathematical function using the RMSProp algorithm
#'
#' This functions uses the RMSProp algorithm to find the minimum of a (multi-)
#' dimensional mathematical function. The algorithm searches for the.
#' The 'eps' factor avoids numerical issues of dividing by 0. The 'step.size'
#' scales the movement into the single coordinate direction.
#'
#' @import numDeriv
#' @importFrom magrittr "%>%"
#'
#' @param f a (multi-) dimensional function to be eptimized.
#' @param x0 the starting point of the optimization.
#' @param max.iter the maximum number of iterations performed in the optimization.
#' @param step.size the step size (sometimes referred to as 'learn-rate') of the optimization.
#' @param stop.grad the stop-criterion for the gradient change.
#'
#' @export
RMSprop = function(f, x0, max.iter = 100, gamma = 0.9,step.size = 0.1, stop.grad = .Machine$double.eps){

  if (!is.function(f)) stop("f is not a function")
  if (is.na(f(x0))) stop("Dimensions of function and starting point x0 do not match")

  #Initialize theta matrix and default parameter
  errorObs =logical(1L)
  theta = matrix(0, nrow = (length(x0)+2), ncol = max.iter)
  theta[1:length(x0), 1] = x0
  theta[length(x0)+1, 1] = f(x0)

  #Matrix with mean values of previous gradients
  s = matrix(0, nrow = length(x0), ncol = max.iter)
  s[,1] = grad(f, theta[1:length(x0), 1])^2
  eps = 0.0001

  for (i in 2:max.iter) {

    try = tryCatch({
    #Calculate gradient
    nabla = grad(f, theta[1:length(x0), i-1])

    },
    error = function(contd) {

      errorObs <<- TRUE

    }, finally = {
      if (errorObs == TRUE) {
        warning(c("Error RMSprop: Error in gradient calculation. Please choose different set of parameters.",
                  "Often a smaller step size fixes this issue."))
      }
    })
    #Check if stop-criterion already reached
    if (all(abs(nabla) < stop.grad)) {
      i = i-1
      break
    }

    #Calculate expectation (here arithmetic mean) of previous gradients
    s[, i] = gamma*mean(s[, i-1]) + (1-gamma)*nabla^2


    #Determine new point and calculate function value
    theta[, i] = theta[, i-1] - (step.size)/((s[, i-1] + eps)^(1/2))*nabla
    theta[length(x0)+1, i] = f(theta[1:length(x0), i])
    }



  #Return results
  out = apply(matrix(seq(1:length(x0))), 1, function(x) return(theta[x, ])) %>%
    as.data.frame()

  names(out) = paste0("x", 1:(ncol(out)))
  out = cbind(out, y = theta[length(x0)+1, ])

  return(list(algorithm = "RMSprop", results = out, niter = i, optimfun = f, errorOccured = errorObs))
}