search for: fnk

...ns. As an (artificial) example, the following is the Rosenbrock function, where Gaussian noise of standard deviation `sd = 0.01` is added to the function value. fn <- function(x) (1+rnorm(1, sd=0.01)) * adagio::fnRosenbrock(x) To smooth out the noise, define another function `fnk(x, k = 1)` that calls `fn` k times and returns the mean value of those k function applications. fnk <- function(x, k = 1) { # fnk(x) same as fn(x) rv = 0.0 for (i in 1:k) rv <- rv + fn(x) return(rv/n) } When we apply several optimization solvers to this n...

...! for m when values of n and k are provided n1<-c(10,13,18,30,60,100,500)         # values of n kx<-seq(1,7,1);                               # values of k slv2<-lapply(n1,function(n){    slv1<-lapply(kx,function(k){              lhs<-function(m)              {                 fnk<-factorial(n-m)*factorial(n-k)-                       0.5*factorial(n-m-k)*factorial(n);                 return(fnk);              }              un2<-uniroot(lhs,c(0,n))              un1<-un2$root/n;            return(rbind(un1));     });     rjbk<-data.frame(do.call(cbind,slv1));    ...

Hi, I was trying to have a graph whose axes are of the following type: X axis: n and Y axis: var[U ((a,b) in suffix, and (n,d) in the power)]. U ((a,b) in suffix, and (n,d) in the power)- U^(n,d) _ (a,b). Actually I require many plots involving different values of a,b,n,d, so need to keep this complicated notation. The code I used: plot(n, hn$h_pg,

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