Hi Rui, I was not aware of this function, however it looks like it should work for me. Many thanks for this intuition.
However, in my optimization there are 3 constraints as below, 1) sum of all parameters should be 1 2) all parameters should be positive 3) last parameter should be greater than 2.05/100 I can see that the constraint #2 is implemented in the function argument lower = rep(0.01, 21), How can I impose other 2 constraints? Many thanks, On Sat, 29 Mar 2025 at 01:49, Rui Barradas <ruipbarra...@sapo.pt> wrote: > Às 13:59 de 28/03/2025, Daniel Lobo escreveu: > > Hi Duncan, > > > > Thanks for your comment, I agree with that. > > > > But, how it can be justified that an Optimizer gives a result which is > > inferior to the starting value? At most, resulting value can remain at > the > > same level, isnt it? > > > > On Fri, 28 Mar 2025 at 14:34, Duncan Murdoch <murdoch.dun...@gmail.com> > > wrote: > > > >> I haven't run your code, but since Kendall correlation is based on > >> ranks, your Fn is probably locally constant with jumps when the ranks > >> change. That's a really hard kind of function to maximize, and the > >> algorithm used by fmincon is not appropriate to do it. > >> > >> Sorry, but I don't know if there is an R function that can do > >> constrained discrete maximization. > >> > >> Duncan Murdoch > >> > >> On 2025-03-27 2:35 p.m., Daniel Lobo wrote: > >>> Hi, > >>> > >>> I have below minimization problem > >>> > >>> > >>> MyDat = structure(list(c(50L, 0L, 0L, 50L, 75L, 100L, 50L, 0L, 50L, 0L, > >>> 25L, 50L, 50L, 75L, 75L, 75L, 0L, 75L, 75L, 75L, 0L, 25L, 75L, > >>> 75L, 0L, 75L, 100L, 0L, 25L, 100L), c(75L, 0L, 0L, 50L, 100L, > >>> 50L, 75L, 75L, 100L, 25L, 0L, 25L, 100L, 0L, 50L, 0L, 25L, 25L, > >>> 100L, 75L, 0L, 0L, 0L, 50L, 0L, 75L, 75L, 0L, 50L, 25L), c(50L, > >>> 0L, 0L, 0L, 100L, 25L, 0L, 0L, 25L, 50L, 0L, 25L, 75L, 50L, 100L, > >>> 50L, 0L, 75L, 25L, 50L, 0L, 0L, 25L, 0L, 50L, 100L, 100L, 0L, > >>> 75L, 50L), c(25L, 0L, 0L, 75L, 75L, 25L, 50L, 50L, 100L, 25L, > >>> 0L, 100L, 50L, 25L, 100L, 25L, 25L, 100L, 50L, 100L, 0L, 0L, > >>> 100L, 50L, 0L, 50L, 75L, 0L, 50L, 25L), c(50L, 0L, 0L, 75L, 75L, > >>> 75L, 25L, 25L, 0L, 100L, 0L, 25L, 25L, 75L, 100L, 0L, 25L, 0L, > >>> 75L, 25L, 25L, 25L, 75L, 25L, 0L, 75L, 100L, 0L, 100L, 100L), > >>> c(50L, 0L, 0L, 50L, 100L, 25L, 25L, 25L, 50L, 50L, 0L, 50L, > >>> 75L, 0L, 100L, 50L, 25L, 100L, 50L, 75L, 0L, 0L, 50L, 25L, > >>> 0L, 100L, 100L, 0L, 75L, 50L), c(50L, 0L, 0L, 50L, 75L, 25L, > >>> 75L, 50L, 100L, 25L, 0L, 75L, 25L, 0L, 50L, 0L, 50L, 75L, > >>> 100L, 75L, 0L, 0L, 100L, 0L, 0L, 50L, 75L, 0L, 100L, 100L > >>> ), c(25L, 75L, 50L, 25L, 75L, 50L, 100L, 75L, 100L, 25L, > >>> 0L, 75L, 25L, 50L, 25L, 25L, 75L, 75L, 100L, 75L, 75L, 100L, > >>> 75L, 25L, 0L, 75L, 75L, 0L, 75L, 100L), c(55L, 30L, 20L, > >>> 30L, 45L, 30L, 30L, 30L, 70L, 30L, 10L, 45L, 45L, 45L, 45L, > >>> 30L, 30L, 55L, 45L, 45L, 30L, 30L, 30L, NA, 30L, 55L, 45L, > >>> 20L, 45L, 70L), c(85L, 40L, 40L, 40L, 55L, 40L, 20L, 30L, > >>> 30L, 30L, 20L, 30L, 70L, 40L, 85L, 55L, 30L, 40L, 30L, 55L, > >>> 20L, 30L, 55L, 0L, 40L, 55L, 70L, 40L, 85L, 70L), c(45L, > >>> 45L, 0L, 45L, 45L, 45L, 0L, 0L, 100L, 45L, 0L, 100L, 45L, > >>> 45L, 100L, 45L, 45L, 100L, 45L, 45L, 45L, 45L, 25L, 45L, > >>> 0L, 100L, 45L, 0L, 45L, 45L), c(55L, 45L, 45L, 45L, 55L, > >>> 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 55L, 55L, 45L, > >>> 55L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 55L, 45L, 45L, 45L, > >>> 45L), c(100L, 100L, 50L, 100L, 100L, 100L, 100L, 100L, 100L, > >>> 100L, 50L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, > >>> 100L, 100L, 100L, 100L, 50L, 100L, 100L, 100L, 100L, 100L, > >>> 100L), c(100L, 25L, 25L, 0L, 100L, 60L, 0L, 0L, 25L, 60L, > >>> 0L, 60L, 100L, 60L, 100L, 100L, 25L, 100L, 60L, 100L, 100L, > >>> 60L, 100L, 60L, 100L, 100L, 100L, 100L, 60L, 60L), c(0L, > >>> 0L, 50L, 50L, 100L, 100L, 0L, 0L, 100L, 100L, 0L, 100L, 100L, > >>> 0L, 100L, 100L, 0L, 100L, 100L, 100L, 100L, 100L, 100L, 0L, > >>> 100L, 100L, 100L, 100L, 100L, 100L), c(40L, 100L, 40L, 100L, > >>> 100L, 40L, 100L, 100L, 100L, 40L, 100L, 100L, 100L, 100L, > >>> 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, > >>> 100L, 100L, 100L, 0L, 100L, 100L), c(100L, 100L, 100L, 100L, > >>> 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, > >>> 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, -10L, > >>> 100L, 100L, 100L, -10L, 100L, 100L), c(70L, 0L, 25L, 0L, > >>> 100L, 25L, 0L, 0L, 0L, 45L, 0L, 25L, 100L, 100L, 100L, 100L, > >>> 0L, 70L, 0L, 100L, 45L, 45L, 0L, 0L, 100L, 100L, 100L, 0L, > >>> 100L, 100L), c(55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, > >>> 55L, 55L, 55L, 55L, 55L, 55L, 55L, 20L, 55L, 20L, 55L, 20L, > >>> 20L, 100L, 55L, 55L, 55L, 55L, 0L, 55L, 55L), c(65L, 65L, > >>> 100L, 65L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, > >>> 100L, 100L, 100L, 100L, 65L, 100L, 100L, 100L, 65L, 100L, > >>> 0L, 65L, 100L, 100L, 100L, 100L, 100L, 100L), c(85L, 85L, > >>> 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 56L, 85L, > >>> 100L, 85L, 85L, 85L, 0L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, > >>> 85L, 28L, 56L, 56L)), row.names = c(NA, -30L), class = > "data.frame") > >>> > >>> Fn = function(Wts) return(-Kendall::Kendall(1:Nobs, > >>> rank(-as.vector(as.matrix(MyDat) %*% matrix(Wts, nc = 1)[, 1, drop = > >>> T])))$tau[1]) > >>> q1 = pracma::fmincon(c(0.12, 0.04, 0.07, 0.03, 0.06, 0.07, 0.07, 0.04, > >>> 0.09, 0.08, 0.02, 0.02, 0.03, 0.06, 0.02, 0, 0.07, 0.05, 0.02, 0.02, > >> 0.02), > >>> fn = Fn, > >>> A = matrix(c(rep(0, 20), -1), nrow = 1), b = -2.05/100, > Aeq = > >>> matrix(c(rep(1, 20), 1), nrow = 1), beq = 1, > >>> lb = rep(0.01, 21), > >>> tol = 1e-16, maxfeval = 10000000, maxiter = 5000000) > >>> > >>> > >>> However with above code, I got sub-optimal value in terms of > minimization > >>> of the objective function: > >>> > >>> q1$value > >>> #0.1632184 > >>> Fn(c(0.12, 0.04, 0.07, 0.03, 0.06, 0.07, 0.07, 0.04, 0.09, 0.08, 0.02, > >>> 0.02, 0.03, 0.06, 0.02, 0, 0.07, 0.05, 0.02, 0.02, 0.02)) > >>> #0.1586207 > >>> > >>> Could you please help me to understand what went wrong with my code and > >> how > >>> to correct that? > >>> > >>> [[alternative HTML version deleted]] > >>> > >>> ______________________________________________ > >>> 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. > >> > >> > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > 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. > Hello, > > I don't know if this is relevant a package GA - genetic algorithms - > gets solutions above the starting value. > > > > Fn <- function(Wts) return( > -Kendall::Kendall( > 1:Nobs, > rank(-as.vector(as.matrix(MyDat) %*% matrix(Wts, nc = 1)[, 1, drop > = T])) > )$tau[1] > ) > > Nobs <- nrow(MyDat) > StartingValue <- c(0.12, 0.04, 0.07, 0.03, 0.06, 0.07, 0.07, > 0.04, 0.09, 0.08, 0.02, 0.02, 0.03, 0.06, > 0.02, 0, 0.07, 0.05, 0.02, 0.02, 0.02) > > library(GA) > #> Loading required package: foreach > #> Loading required package: iterators > #> Package 'GA' version 3.2.4 > #> Type 'citation("GA")' for citing this R package in publications. > #> > #> Attaching package: 'GA' > #> The following object is masked from 'package:utils': > #> > #> de > > set.seed(2025) > g1 <- ga( > type = "real-valued", > fitness = \(x) Fn(x), > lower = rep(0.01, 21), > upper = rep(1, 21L), > maxiter = 100L > ) > dim(g1@solution) > #> [1] 21 21 > apply(g1@solution, 1L, Fn) > #> [1] 0.2735632 0.2735632 0.2735632 0.2735632 0.2735632 0.2735632 > 0.2735632 > #> [8] 0.2735632 0.2735632 0.2735632 0.2735632 0.2735632 0.2735632 > 0.2735632 > #> [15] 0.2735632 0.2735632 0.2735632 0.2735632 0.2735632 0.2735632 > 0.2735632 > Fn(StartingValue) > #> [1] 0.1586207 > > suggestions <- g1@solution > g2 <- ga( > type = "real-valued", > fitness = function(x) Fn(x), > lower = rep(0.01, 21), > upper = rep(1, 21L), > suggestions = suggestions, > maxiter = 100L > ) > > dim(g2@solution) > #> [1] 41 21 > apply(g2@solution, 1L, Fn) > #> [1] 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 > 0.2873563 > #> [8] 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 > 0.2873563 > #> [15] 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 > 0.2873563 > #> [22] 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 > 0.2873563 > #> [29] 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 > 0.2873563 > #> [36] 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 0.2873563 > Fn(StartingValue) > #> [1] 0.1586207 > > > Hoep this helps, > > Rui Barradas > > > -- > Este e-mail foi analisado pelo software antivírus AVG para verificar a > presença de vírus. > www.avg.com > [[alternative HTML version deleted]] ______________________________________________ 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.
