Find a value that equates the averages of two group within a reasonable tolerance level

[Patrick Frimpong Manso]

Hello everyone,
Below is the Stata code that does exactly what I am looking for but the looping takes a very long time. Given the set up it may weeks to complete the entire 311,000 iterations.

Essentially what I am trying to achieve is to determine the value `x' that ensures that the average of YIndem (avg1) and average of WeathIndem (avg2) are similar within a reasonable tolerance level.
The is equivalent to "Goal seek" or "Solver" in excel but looping over will take weeks. I was hoping I can use Mata and optimize function solve such a problem.

* Example generated by -dataex-. For more info, type help dataex
clear
input double YieldIndex float SWCIndex double conv_factor
 .9253806198611291  .9500761 .6226526011135668
 .7824689060953668 1.0973326 .6226526011135668
1.6404846532462987 1.2310717 .6226526011135668
 1.135791047177306 1.0770227 .6226526011135668
 .5527689833452102  .9240186 .6226526011135668
1.2941161617887758 1.0016987 .6226526011135668
 .7024821439818003  .9564832 .6226526011135668
1.4052729869411842 1.1745064 .6226526011135668
 1.316751439047902 1.1611431 .6226526011135668
1.2891894358766425 1.0278761 .6226526011135668
 .9909411357309351  .8795984 .6226526011135668
1.3410032409929848 1.3822783 .6226526011135668
1.0861081686220508  .8990123 .6226526011135668
 .6649476376589332  .8974741 .6226526011135668
 .3341956330126147  .6080579 .6226526011135668
  .909491036549925  .9555679 .6226526011135668
1.2837267687754703 1.0622913 .6226526011135668
 .9087439508922139 1.1295747 .6226526011135668
  .853106566143711 1.3886855 .6226526011135668
 .4042373654867438 1.0403242 .6226526011135668
end

gen Ydeduct=.
gen AvgYIndem=.
gen AvgWIndem=.
gen YIndem=.
gen WeathIndem=.

forval a=1/311619{

replace WeathIndem=max(0,min(1,(1-0.05-SWCIndex)*conv_factor)) 
    quietly summarize WeathIndem 
    local avg2 = r(mean)
      
      local x=0
      local tol = 0.00001     

      local diff =10

while `diff'>`tol'{

replace YIndem = max(0,min(1,1-`x'-YieldIndex)) if
    quietly summarize YIndem 
    local avg1 = r(mean)

      local diff=abs(`avg1' - `avg2')
      
   local x =`x' + 0.0001
   
   di "`x'"
   di "`avg1'"
   di "`avg2'"
   
   di "`diff'"

}

replace Ydeduct=`x' 
replace AvgYIndem= `avg1' 
replace AvgWIndem=`avg2' 
}

This is my attempt using Mata and Optimize function

mata
void  myfunc(todo, x, lnf, g, H) 
{
    
    
    SWCIndex=st_data(., "SWCIndex")
    conv_factor=st_data(.,"conv_factor")
    YieldIndex=st_data(.,"YieldIndex")
    
    variable1=min((1, max((0, (0.95 :- SWCIndex) :* conv_factor))))
    variable2=min((1, max((0, 1 :-x :-YieldIndex) )))
    

    mean_v1=mean(variable1)
    mean_v2=mean(variable2)
    
    diff_sq=(mean_v1-mean_v2)^2
    
    lnf=-diff_sq
    
    *st_numscalar("lnf",lnf)

}

S  = optimize_init()
optimize_init_which(S, "max")
optimize_init_evaluator(S, &myfunc())
optimize_init_params(S, 0)
bh = optimize(S)

bh
end

However, I get these errors

 
    myfunc():  3200  conformability error       opt__calluser0_d():     -  function returned error       opt__d0_calluser():     -  function returned error     deriv__call1user_d():     -  function returned error  _deriv__compute_value():     -  function returned error                 _deriv():     -  function returned error        opt__eval_nr_d0():     -  function returned error              opt__eval():     -  function returned error opt__looputil_iter0_common():     -  function returned error opt__looputil_iter0_nr():     -  function returned error           opt__loop_nr():     -  function returned error              opt__loop():     -  function returned error               optimize():     -  function returned error                  <istmt>:     -  function returned error r(3200);

Any help or direction to any resource that can help me will be appreciated.