rfunctionfixed-point-iteration

R fixed point of a function


I am looking for a fixed point x when f(x)=x of a function, ofcourse numerically, but I have no idea how to solve it with R, I am trying with fsolve with following code, but possibly its not the right way to write this...I am not getting anything...Many thanks in advance

library(pracma)  
p<- c(x=0.1, y=0.1)
f1 <- function(p) {
                expPfLeft<- (160*p[1]) + ((1-p[1])*200)
                expPfRight<- (10*p[1])+ ((1-p[1])*370)
                expPfTop <- (200*p[2]) + ((1-p[2])*160)
                expPfBottom <- (370*p[2]) + ((1-p[2])*10) 

                return(c (exp(2*expPfLeft)/((exp(2*expPfLeft)+exp(2*expPfRight))) , 
                          exp(2*expPfTop)/((exp(2*expPfTop))+(exp(2*expPfBottom))) ) )


        }


fsolve(f1,p)

Solution

  • Assuming your function is defined correctly. You are looking for f(p[1], p[2]) = c(p[1], p[2])

    You can create fix your answer by changing the return statement to:

    return(c(exp(2*expPfLeft)/((exp(2*expPfLeft)+exp(2*expPfRight))), exp(2*expPfTop)/((exp(2*expPfTop))+(exp(2*expPfBottom)))) - p)

    We can observe that your optimization function are actually two independent functions. p[1] is used in Left and Right. p[2] is used in Top and Bottom.

    We can separate your functions.

    f.x <- function(p) {
      expPfLeft<- (160*p) + ((1-p)*200)
      expPfRight<- (10*p)+ ((1-p)*370)
      return(exp(2*expPfLeft)/((exp(2*expPfLeft)+exp(2*expPfRight))) - p)
    }
    
    f.y <- function(p) {
      expPfTop <- (200*p) + ((1-p)*160)
      expPfBottom <- (370*p) + ((1-p)*10) 
      return(exp(2*expPfTop)/((exp(2*expPfTop))+(exp(2*expPfBottom))) - p)
    }
    

    Simplifying your expression so we can cheat a little for the starting value

    enter image description here

    We can see that there is only one approximate approximate solution at x = 1.

    fsolve(f.x, 1)
    
    $x
    [1] 1
    
    $fval
    [1] 0
    

    And similarly for the second function, there is a solution at 0.4689.

    fsolve(f.y, 0.1)
    
    $x
    [1] 0.4689443
    
    $fval
    [1] 4.266803e-09
    

    Doing all this defeats the purpose of optimization, and leads me to believe that your optimization function is mis-defined.