R/doc_mbo_parallel.R

In mlrMBO: Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions

#' @title Parallelization in mlrMBO
#'
#' @description
#' In mlrMBO you can parallelize the tuning on two different levels to speed up computation:
#' \itemize{
#'   \item{\code{mlrMBO.feval}}{Multiple evaluations of the target function.}
#'   \item{\code{mlrMBO.propose.points}}{Optimization of the infill criteria if multiple are used (e.g. ParEGO and ParallelLCB)}
#' }
#' Internally the evaluation of the target function is realized with the R package parallelMap.
#' See the mlrMBO tutorial and the Github project pages of parallelMap for instructions on how to set up parallelization.
#' The different levels of parallelization can be specified in \code{parallelStart*}.
#' Details for the levels mentioned above are given below:
#' \itemize{
#' \item{Evaluation of the objective function can be parallelized in cases multiple points are to be evaluated at once. These are: evaluation of the initial design, multiple proposed points per iteration and evaluation of the target function in \code{\link{exampleRun}}. (Level: \code{mlrMBO.feval})}
#' \item{Model fitting / point proposal - in some cases where independent, expensive operations are performed. (Level: \code{mlrMBO.propose.points})}
#' }
#' Details regarding the latter:
#' \describe{
#' \item{single-objective MBO with LCB multi-point}{Parallel optimization of LCBs for the lambda-values.}
#' \item{Multi-objective MBO with ParEGO}{Parallel optimization of scalarization functions.}
#' }
#'
#' @name mbo_parallel
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