recursion - how to make double for loops faster in R -


i trying below calculation using r. function recursive , uses double loop calculate values of "result" matrix. there method replace loops or achieve if condition faster? 

x<-rnorm(2400,0, 3) y<-rnorm(400,0,3) no_row<-length(x) no_col<-length(y) input<-matrix(data=1,nrow = no_row, ncol = no_col) result<-matrix(nrow = no_row, ncol = no_col) calculation<-function(x,y) {   for(i in 1:no_row)  {   for(j in 1:no_col)   {     z<-exp(x[i]-y[j])     result[i,j]<-(z/1+z)    }  }   new_x<-x-1  new_y<-y-1  residual<-input-result      sq_sum_residulas<-sum((rowsums(residual, na.rm = t))^2)  if(sq_sum_residulas>=1){calculation(new_x,new_y)}   else(return(residual)) } output<-calculation(x,y)

the outer function tool looking for.

compare these 2 functions generate result matrix

x<-rnorm(100,0, 3) y<-rnorm(100,0,3)  calculation<-function(x,y) {     result <- matrix(nrow = length(x), ncol = length(y))   for(i in seq_along(x))   {     for(j in seq_along(y))     {       z<-exp(x[i]-y[j])       result[i,j]<-(z/1+z)     }   }    result }   calculation2 <- function(x, y){   result <- outer(x, y, function(x, y) { z <- exp(x - y); z / 1 + z})   result }  library(microbenchmark) microbenchmark(   calculation(x, y),   calculation2(x, y) )  unit: microseconds                expr     min       lq      mean   median        uq      max neval   calculation(x, y) 1862.40 1868.119 1941.5523 1871.490 1876.1825 8375.666   100  calculation2(x, y)  466.26  469.192  515.3696  471.392  480.9225 4481.371   100

that discrepancy in time seems grow length of vectors increases.

note, solve speed double for loop, there seem other issues in function. isn't clear me trying do, or why calling calculation within itself. have written, there no changes x , y before gets calling again, stuck in loop forever, if worked @ (it doesn't on machine)


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