Why does 'could not find feasible values' error msg pops up while running ML command?

[Nayeon Kang]

Hi all,
I am trying to define a program and find parameters (para_a, para_b) optimizing an optimizing function. What I want to do is minimize the difference between beta dist. CDF and empirically observed CDF. Here is my code.
1) defining a program

program define myobjfunc, rclass
set trace on
    args lnf para_a para_b
    tempvar  observed_cdf diff sum_diff temp_cdf current_sum_diff
    local xl .
    local xr .
    local lower_bound = round(assigned_value_lower, 0.01)
    local upper_bound = round(assigned_value_upper, 0.01)
    local total_sum_obsCDF 0
    local total_sum_diff 0 
    local pred_cdf 0 



forvalues i = 1/10 {


    gen temp_value = qsp7dens_`i'_new
    quietly sum temp_value
    local this_sum = r(sum)
    local total_sum_obsCDF = `total_sum_obsCDF' + `this_sum'
    drop temp_value


    if `i' == 10 {
        local xl -0.16
        local xr -0.12
    }
    else if `i' == 9 {
        local xl -0.12
        local xr -0.08
    }
    else if `i' == 8 {
        local xl -0.08
        local xr -0.04
    }
    else if `i' == 7 {
        local xl -0.04
        local xr -0.02
    }
    else if `i' == 6 {
    local xl -0.02
    local xr 0.00
    }
    else if `i' == 5 {
    local xl 0.00
    local xr 0.02
    }
    else if `i' == 4 {
    local xl 0.02
    local xr 0.04
    }
    else if `i' == 3 {
    local xl 0.04
    local xr 0.08
    }
    else if `i' == 2{
    local xl 0.08
    local xr 0.12
    }

    else if `i' == 1 {
    local xl 0.12
    local xr 0.16
    }
    di "xl is " `xl', "xr is "`xr'

    if (`xr' > `upper_bound') {
        local temp_cdf = 1
    }

    else if (`lower_bound' <= `xl' & `xl' < `upper_bound' & `lower_bound' < `xr' & `xr' <= `upper_bound') {
    
        local temp_cdf = ibeta(`para_a', `para_b', (`xr' - `lower_bound') / (`upper_bound' - `lower_bound'))
    }

    else if (`xl' < `lower_bound') {
        local temp_cdf = 0
    }
 
    local pred_cdf = `temp_cdf'
    local diff = (`total_sum_obsCDF' - `pred_cdf')^2
    local total_sum_diff = `total_sum_diff' + `diff'
        
}
    local lnf = -`total_sum_diff'

end

2) I want to maximize -`total_sum_diff' stored in local lnf. Conditions for para_a and para_b are described below, and initial values of para_a and para_b are also set.

constraint 1 para_a > 1
constraint 2 para_b > 1


levelsof uniqueid, local(userids)


foreach id of local userids{
 
    preserve
    // I want to loop id-by-id so generate variable `is_current_user'. For current user, the ml is operated.
    gen is_current_user = (uniqueid == `id')
    
    keep if is_current_user & No_dens>= 3

   // ml
    ml model lf myobjfunc (para_a:, freeparm) (para_b:, freeparm), constraints(1 2)
    ml init /para_a=1.04 /para_b=1.5
    ml maximize


    ml check
    // store optimal values of para_a and para_b in original (.dta) dataset.
    matrix opt_values = e(b)
    scalar opt_a_temp = opt_values[1,1]
    scalar opt_b_temp = opt_values[1,2]
    
    // opt_a and opt_b: variables defined in .dta
    replace opt_a = opt_a_temp if is_current_user
    replace opt_b = opt_b_temp if is_current_user
    

    restore
}

As myobjfunc is ended, a msg (could not find feasible values) pops up. Does it just mean that ml command cannot find optimal values? Or is there anything wrong with my code?