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### ** Examples ### Minimize the Hartmann6 function hartmann6 <- function(x) { n <- length(x) a <- c(1.0, 1.2, 3.0, 3.2) A <- matrix(c(10.0, 0.05, 3.0, 17.0, 3.0, 10.0, 3.5, 8.0, 17.0, 17.0, 1.7, 0.05, 3.5, 0.1, 10.0, 10.0, 1.7, 8.0, 17.0, 0.1, 8.0, 14.0, 8.0, 14.0), nrow=4, ncol=6) B <- matrix(c(.1312,.2329,.2348,.4047, .1696,.4135,.1451,.8828, .5569,.8307,.3522,.8732, .0124,.3736,.2883,.5743, .8283,.1004,.3047,.1091, .5886,.9991,.6650,.0381), nrow=4, ncol=6) fun <- 0.0 for (i in 1:4) { fun <- fun - a[i] * exp(-sum(A[i,]*(x-B[i,])^2)) } return(fun) } S <- directL(hartmann6, rep(0,6), rep(1,6), nl.info = TRUE, control=list(xtol_rel=1e-8, maxeval=1000))
Call: nloptr(x0 = x0, eval_f = fn, lb = lower, ub = upper, opts = opts) Minimization using NLopt version 2.7.1 NLopt solver status: 5 ( NLOPT_MAXEVAL_REACHED: Optimization stopped because maxeval (above) was reached. ) Number of Iterations....: 1000 Termination conditions: stopval: -Inf xtol_rel: 1e-08 maxeval: 1000 ftol_rel: 0 ftol_abs: 0 Number of inequality constraints: 0 Number of equality constraints: 0 Current value of objective function: -3.32236800687327 Current value of controls: 0.2016884 0.1500025 0.4768667 0.2753391 0.311648 0.6572931
## Number of Iterations....: 500 ## Termination conditions: stopval: -Inf ## xtol_rel: 1e-08, maxeval: 500, ftol_rel: 0, ftol_abs: 0 ## Number of inequality constraints: 0 ## Number of equality constraints: 0 ## Current value of objective function: -3.32236800687327 ## Current value of controls: ## 0.2016884 0.1500025 0.4768667 0.2753391 0.311648 0.6572931