R/linear_gd_optim_old.R

In lucaaiello/myOpt: My Optimization Package

#' linear_gd_optim_old
#'
#' This function provides a personalized way for optimizing functions
#'
#' @param par Initial values.
#' @param X Data predictiors.
#' @param Y Response variables.
#' @param tolerance Value for the stopping criterion. The default value is set to 1e-6.
#' @param maxit Maximum iterations allowed. The default value is set to 10000.
#' @param stepsize Length of the stepsize parameter. The default value is set to 1e-3.
#' @param verbose If set TRUE the function produce messages during the computation.
#'
#' @return Vector containing the estimated parameters.
#' @export
#'

linear_gd_optim_old <- function(par, X, Y,
                                tolerance=1e-6, maxit=10000, stepsize=1e-3,
                                verbose=T){

  I <- dim(X)[2] # numbers of predictors (including the intercept)

  it <- 1  # iteration index iniziation
  err <- 1 # error iniziation

  par_new <- vector(mode = "numeric", length = I) # vector for the update in the cicle

  while (err > tolerance & it < maxit) {

    grad <- my_grad_old(par, X, Y) # gradient computation with the current values of the parameters

    for (i in 1:I) {

      par_new[i] <- par[i] - stepsize * grad[i] # update of the parameters

    }

    err <- max(abs(par_new - par)) # computation of the error
    it <- it + 1                   # updating the iteration index

    par <- par_new # assigning the new value to the old one as to begin a possible new iteration

  }

  names(par_new) <- c('(Intercept)', paste('x',1:(dim(X)[2]-1),sep=""))

  return(par_new)

}