Here some kind of a brute force attack: #brute force solution, only working with relative small subsets: n <- 200 elem <- 3 target <- 200
x <- rnorm(n,100,10) x.combinations <- combn(x,elem) sums <- apply(x.combinations,2,function(x) (sum(x)-target)^2) ans <- (x.combinations[,which.min(sums)]) #seems to work for larger subsets: require(gtools) x.combinations <- combinations(n, elem) sums <- apply(x.combinations,1,function(sel) (sum(x[sel])-target)^2) print(x[x.combinations[which.min(sums),]]) Although it takes a lot of computation time, you are sure you will find the minimum. Bart -- View this message in context: http://n4.nabble.com/Solving-an-optimization-problem-selecting-an-optimal-subset-tp1446084p1457514.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
