# User-friendly wrapper of the functions `fastEGO.nsteps` and `TREGO.nsteps`. Generates initial DOEs and kriging models (objects of class `km`), and executes `nsteps` iterations of either EGO or TREGO. ```r easyEGO( fun, budget, lower, upper, X = NULL, y = NULL, control = list(trace = 1, seed = 42), n.cores = 1, ... ) ``` ## Arguments - `fun`: scalar function to be minimized, - `budget`: total number of calls to the objective and constraint functions, - `lower`: vector of lower bounds for the variables to be optimized over, - `upper`: vector of upper bounds for the variables to be optimized over, - `X`: initial design of experiments. If not provided, X is taken as a maximin LHD with budget/3 points - `y`: initial set of objective observations $f(X)$. Computed if not provided. - `control`: an optional list of control parameters. See "Details". - `n.cores`: number of cores for parallel computation - `...`: additional parameters to be given to `fun` ## Returns A list with components: * `par`: the best feasible point * `values`: a vector of the objective and constraints at the point given in `par`, * `history`: a list containing all the points visited by the algorithm (`X`) and their corresponding objectives (`y`). * `model`: the last GP model, class `km` * `control`: full list of control values, see "Details" * `res`: the output of either `fastEGO.nsteps` or `TREGO.nsteps` ## Details Does not require knowledge on kriging models (objects of class `km`) The `control` argument is a list that can supply any of the following components: * `trace`: between -1 and 3 * `seed`: to fix the seed of the run * `cov.reestim`: Boolean, if TRUE (default) the covariance parameters are re-estimated at each iteration * `model.trend`: trend for the GP model * `lb, ub`: lower and upper bounds for the GP covariance ranges * `nugget`: optional nugget effect * `covtype`: covariance of the GP model (default "matern5_2") * `optim.method`: optimisation of the GP hyperparameters (default "BFGS") * `multistart`: number of restarts of BFGS * `gpmean.trick, gpmean.freq`: Boolean and integer, resp., for the gpmean trick * `scaling`: Boolean, activates input scaling * `warping`: Boolean, activates output warping * `TR`: Boolean, activates TREGO instead of EGO * `trcontrol`: list of parameters of the trust region, see `TREGO.nsteps` * `always.sample`: Boolean, activates force observation even if it leads to poor conditioning ## Examples ```r library(parallel) library(DiceOptim) set.seed(123) ######################################################### ### 10 ITERATIONS OF TREGO ON THE BRANIN FUNCTION, #### ### STARTING FROM A 9-POINTS FACTORIAL DESIGN #### ######################################################## # a 9-points factorial design, and the corresponding response ylim=NULL fun <- branin; d <- 2 budget <- 5*d lower <- rep(0,d) upper <- rep(1,d) n.init <- 2*d control <- list(n.init=2*d, TR=TRUE, nugget=1e-5, trcontrol=list(algo="TREGO"), multistart=1) res1 <- easyEGO(fun=fun, budget=budget, lower=lower, upper=upper, control=control, n.cores=1) par(mfrow=c(3,1)) y <- res1$history$y steps <- res1$res$all.steps success <- res1$res$all.success sigma <- res1$res$all.sigma ymin <- cummin(y) pch <- rep(1, length(sigma)) col <- rep("red", length(sigma)) pch[which(!steps)] <- 2 col[which(success)] <- "darkgreen" pch2 <- c(rep(3, n.init), pch) col2 <- c(rep("black", n.init), col) plot(y, col=col2, ylim=ylim, pch=pch2, lwd=2, xlim=c(0, budget)) lines(ymin, col="darkgreen") abline(v=n.init+.5) plot(n.init + (1:length(sigma)), sigma, xlim=c(0, budget), ylim=c(0, max(sigma)), pch=pch, col=col, lwd=2, main="TR size") lines(n.init + (1:length(sigma)), sigma, xlim=c(0, budget)) abline(v=n.init+.5) plot(NA, xlim=c(0, budget), ylim=c(0, 1), main="x0 (coordinates)") for (i in 1:d) { lines(n.init + (1:nrow(res1$res$all.x0)), res1$res$all.x0[,i]) points(n.init + (1:nrow(res1$res$all.x0)), res1$res$all.x0[,i], pch=pch, col=col, lwd=2) } abline(v=n.init+.5) par(mfrow=c(1,1)) pairs(res1$model@X, pch=pch2, col=col2) ``` ## References D.R. Jones, M. Schonlau, and W.J. Welch (1998), Efficient global optimization of expensive black-box functions, **Journal of Global Optimization**, 13, 455-492. ## Author(s) Victor Picheny

easyEGO function