# Optim search/convergence speed

**URL:** <https://discourse.julialang.org/t/optim-search-convergence-speed/97083>\
**Category:** Performance\
**Tags:** optim, speed-optimization\
**Created:** [April 4, 2023, 8:23pm UTC](https://discourse.julialang.org/t/optim-search-convergence-speed/97083 "2023-04-04T20:23:34Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![X\_Chen](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/x_chen/32/47565_2.png) [@X\_Chen](https://discourse.julialang.org/u/X_Chen)\
**Post date:** [April 4, 2023, 8:23pm UTC](https://discourse.julialang.org/t/optim-search-convergence-speed/97083/1 "2023-04-04T20:23:34Z")

</div>

Hello!

I am using Optim.optimize to conduct a MLE algorithm with 6 parameters. The objective function is defined as follows:

```julia
function r_obj(r_para)
    print(r_para)
   df_y_1_p_0.LL_1 = cap_p.((r_prob_0(r_para,df_0.x3).p_1.*r_prob_0(r_para,df_0.x3).den)./r_prob_0(r_para,df_0.x3).denominator)
    df_y_0_p_0.LL_0= cap_p.((r_prob_0(r_para,df_0.x3).p_0.*r_prob_0(r_para,df_0.x3).den)./r_prob_0(r_para,df_0.x3).denominator)
    ll_0=log.(df_y_1_p_0.LL_1)
    ll_1=log.(df_y_0_p_0.LL_0)
    sum_ll=sum(-ll_0)+sum(-ll_1)
    print(sum_ll)
    return (sum_ll)
end

```

and my constraints and initila values are:

```julia
ub=[sqrt(σ_a),1,2π,Inf,Inf,Inf]
lb=[-sqrt(σ_a),-Inf,0,-Inf,-Inf,-Inf]
initial = [0.5,0.6,0.3,-2,1,1]
inner_optimizer = GradientDescent()

```

Then I try to optimize it by

```julia
Optim.optimize(r_obj, lb, ub, initial, Fminbox(inner_optimizer))

```

I compared the printed output value with the value that I calculated around the true parameters, and I can observe that it’s reaching the minimum around the true value. However, the searching steps are not efficient because it only has a tiny move between loops and it didn’t converge for more than 12 hours. I am wondering if my convergence routine has some problems or if there is a way that I can choose a tolerance level better.

Thank you so much!