Hi Duncan, I take your advice.
I posted here in search for a better answer to my problem as I could not get that there. My question is: 1. Why nloptr() is failing where other programs can continue with the same set of data, numbers, and constraints? 2. Is this enough ground to say that nloptr is inferior and user should not use this in complex problems? I wish to get a thoughtful answer to above as my working environment only has the nloptr package installed, and it is an isolated system due to security issues and installation of a new package requires lot lot of approvals and time consuming. BTW, if someone interested here is my original post https://stackoverflow.com/a/79271318/15910619 On Sat, 14 Dec 2024 at 00:15, Duncan Murdoch <murdoch.dun...@gmail.com> wrote: > > You posted a version of this question on StackOverflow, and were given > advice there that you ignored. > > nloptr() clearly indicates that it is quitting without reaching an > optimum, but you are hiding that message. Don't do that. > > Duncan Murdoch > > On 2024-12-13 12:52 p.m., Daniel Lobo wrote: > > library(nloptr) > > > > set.seed(1) > > A <- 1.34 > > B <- 0.5673 > > C <- 6.356 > > D <- -1.234 > > x <- seq(0.5, 20, length.out = 500) > > y <- A + B * x + C * x^2 + D * log(x) + runif(500, 0, 3) > > > > #Objective function > > > > X <- cbind(1, x, x^2, log(x)) > > f <- function(theta) { > > sum(abs(X %*% theta - y)) > > } > > > > #Constraint > > > > eps <- 1e-4 > > > > hin <- function(theta) { > > abs(sum(X %*% theta) - sum(y)) - 1e-3 + eps > > } > > > > Hx <- function(theta) { > > X[100, , drop = FALSE] %*% theta - (120 - eps) > > } > > > > #Optimization with nloptr > > > > Sol = nloptr(rep(0, 4), f, eval_g_ineq = hin, eval_g_eq = Hx, opts = > > list("algorithm" = "NLOPT_LN_COBYLA", "xtol_rel" = 1.0e-8))$solution > > # -0.2186159 -0.5032066 6.4458823 -0.4125948 > ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
