> Reply to "Optimization with constraint" on March 14, 2008 One can get an accurate solutons applying the "Differential Evolution" algorithm as implemented in the DEoptim package:
f2 <- function(x){ if (x[1] + x[2] < 1 || x[1] + x[2] > 1) { r <- Inf } else { r <- x[1]^2 + x[2]^2 } return(r) } lower <- c(0, 0) upper <- c(1, 1) DEoptim(f2, lower, upper, control=list(refresh=200))$bestmem iteration: 200 best member: 0.5 0.5 best value: 0.5 This approach assumes nothing about the gradient, hessian or whatever. And the equality is split into two inequalities assuming no relaxation or penalty. Andreas Klein <klein82517 <at> yahoo.de> wrote: > > Hello. > > I have some problems, when I try to model an > optimization problem with some constraints. > > The original problem cannot be solved analytically, so > I have to use routines like "Simulated Annealing" or > "Sequential Quadric Programming". > > But to see how all this works in R, I would like to > start with some simple problem to get to know the > basics: > > The Problem: > min f(x1,x2)= (x1)^2 + (x2)^2 > s.t. x1 + x2 = 1 > > The analytical solution: > x1 = 0.5 > x2 = 0.5 > > Does someone have some suggestions how to model it in > R with the given functions optim or constrOptim with > respect to the routines "SANN" or "SQP" to obtain the > analytical solutions numerically? > > Again, the simple example should only show me the > basic working of the complex functions in R. > > Hope you can help me. > > With regards > Andreas. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.