R/RcppExports.R

In RMPSS: Recursive Modified Pattern Search on Simplex

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#' Recursive Modified Pattern Search on Simplex
#'
#' 'RMPSolveS' can be used to maximize any function where the set of parameters belong to an unit simplex.
#'
#'
#' @param x0 Vector of Initial Guess provided by User.
#' @param func The Function to be Optimized, should be provided by the User.
#' @param s_init Initial  'Global Step Size'. Default Value is 2. It must be set Less than or Equal to 2.
#' @param rho_1 'Step Decay Rate' for the First Run Only (Default is 2).
#' @param rho_2 'Step Decay Rate' for Second Run Onwards (Default is 2).
#' @param phi Lower Bound for 'Global Step Size'. Default value is \eqn{10^{-6}}.
#' @param max_iter Max Number of Iterations in each 'Run'. Default Value is 10000.
#' @param no_runs Max Number of 'Runs'. Default Value is \eqn{10^{-6}}.
#' @param lambda Sparsity Control Parameter. Default Value is \eqn{10^{-10}}.
#' @param tol_fun Termination Tolerance on when to decrease the 'Global Step Size'. Default Value is \eqn{10^{-6}}. For more accuracy, user may set it to a Smaller Value
#' e.g., \eqn{10^{-20}}. However, for Expensive Objective Functions, for Faster Computation, User should set it to a Larger Value e.g, \eqn{10^{-3}}.
#' @param tol_fun_2 Termination Tolerance on the Difference of Norms of solution points in two Consecutive Runs. Default Value is \eqn{10^{-20}}.
#' However, for Expensive Objective Functions, for Faster Computation, user should set it to a Larger Value e.g, \eqn{10^{-6}}.
#' @param print_output Binary Command to Print Optimized Value of Objective Function after Each Iteration. Default is set as FALSE.
#'
#' @return The Optimal Solution Point.
#'
#' @examples
#'
#' g <- function(y)
#' return(-20 * exp(-0.2 * sqrt(0.5 * (y[1] ^ 2 + (y[2]-1) ^ 2)))
#'          - exp(0.5 * (cos(2 * pi * y[1]) + cos(2 * pi * (y[2]-1))))
#'          + exp(1) + 20)
#'          
#' # global min value is 0, achieved at c(0,1)
#' 
#' starting_point <- c(0.4,0.6)
#'          
#' g(starting_point)
#' solution <- RMPSolveS(starting_point, g)
#' g(solution)
#'            
#' # Example of putting infeasible starting point
#' 
#' g <- function(y)
#' return(-y[1])   # min value is 1, achieved if first coordinate is 1
#'              
#' RMPSolveS(c(1,0.2,56,0.4),g) # starting point NOT on simplex
#'                
#' \donttest{               
#' # Example of 1000 dimensional problem
#' g <- function(y)
#' return(- sum(y^10))
#'                  
#' # min value is -1, achieved if only one
#' # coordinate is equal to 1, rest are 0
#' 
#' RMPSolveS(rep(1 / 1000, 1000), g, print = 1)
#' }
#' 
#'
#' @references
#' \itemize{
#'
#'   \item Das, Priyam \cr
#'    "Recursive Modified Pattern Search on High-dimensional Simplex : A Blackbox Optimization Technique" \cr
#'          (available at `arXiv \url{http://arxiv.org/abs/1604.08636}).
#' }
#'
#' @name RMPSolveS
#' @rdname RMPSolveS
#' @export
NULL

RMPSolveS <- function(x0, func, s_init = 1, rho_1 = 2, rho_2 = 2, phi = 10 ^ (-20), max_iter = 10000, no_runs = 1000, lambda = 10 ^ (-10), tol_fun = 10 ^ (-6), tol_fun_2 = 10 ^ (-20), print_output = 0L) {
    .Call(`_RMPSS_RMPSolveS`, x0, func, s_init, rho_1, rho_2, phi, max_iter, no_runs, lambda, tol_fun, tol_fun_2, print_output)
}