# How to get evaluation number in optimization using NLopt

**URL:** <https://discourse.julialang.org/t/how-to-get-evaluation-number-in-optimization-using-nlopt/114020>\
**Category:** Optimization (Mathematical)\
**Tags:** optimization, nlopt\
**Created:** [May 8, 2024, 8:18pm UTC](https://discourse.julialang.org/t/how-to-get-evaluation-number-in-optimization-using-nlopt/114020 "2024-05-08T20:18:52Z")\
**Posts on this page:** 8\
**Page:** 1

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**Author:** ![mary](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mary/32/53139_2.png) [@mary](https://discourse.julialang.org/u/mary)\
**Post date:** [May 8, 2024, 8:18pm UTC](https://discourse.julialang.org/t/how-to-get-evaluation-number-in-optimization-using-nlopt/114020/1 "2024-05-08T20:18:52Z")

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Hi, I am using Nlopt to do the optimization. in this case i have defined a function for optimization. inside this function the optimizer reads another functions to calculate the objective and gradients. I also want to generate a random number in each iteration. In this case I need the iteration counter in my objective and gradients function to be used. can you please help me how I can return the iteration number in each iteration and use it in other functions?  
so in a nutshell I need the optimizer iteration to : 1- generate identified random number in each iteration,  
2- save the forward analysis results in each iteration.  
3-save the optimized design in each iteration.  
Thank you!

this is the optimization function

```julia
using NLopt

function gf_p_optimize(p_init; r, β, η, TOL=1e-4, MAX_ITER, fem_params)
    ##################### Optimize #################
    opt = Opt(:LD_MMA, fem_params.np)
    opt.lower_bounds = 0
    opt.upper_bounds = 1
    opt.xtol_rel = TOL
    opt.maxeval = MAX_ITER
    opt.max_objective = (p0, grad) -> gf_p(p0, grad; r, β, η, fem_params)
    (g_opt, p_opt, ret) = optimize(opt, p_init)
    @show numevals = opt.numevals # the number of function evaluations
    println("got $g_opt at $p_opt after $numevals iterations (returned $ret)")
    return g_opt, p_opt

end

```

and gf\_p is the function for objective and gradients calculation based on rrule

```julia
function gf_pf(pf_vec; β, η, fem_params)
   
    #generate random number for iteration m
    sample = 2
    Random.seed!(1234);
    diste = LogNormal(log(E_mat),(log(E_mat*0.1))^2)
    Es = rand(diste,sample)
    #forward analysis to solve the equations based on the random value
   #save the result for this iteration
        writevtk(Ω,"resultscellelasti $m",cellfields=["uh"=>uh, "sh"=>sh])
     
        return obj

function rrule(::typeof(gf_pf), pf_vec; β, η, fem_params)
    function U_Disp_pullback(dgdg)
      NO_FIELDS, dgdg * Dgfdpf(pf_vec; β, η, fem_params)
    end
    gf_pf(pf_vec; β, η, fem_params), U_Disp_pullback

end
function Dgfdpf(pf_vec; β, η, fem_params)
    
#solve forward problem again with define random value at iteration m

# solve backward for adjoint
  #calculate the gradient
    return dgfdpfl
end

```

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<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [May 8, 2024, 8:35pm UTC](https://discourse.julialang.org/t/how-to-get-evaluation-number-in-optimization-using-nlopt/114020/2 "2024-05-08T20:35:43Z")

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Hi @Mary,

You can do something like this:

```julia
function gf_p_optimize(p_init; r, β, η, TOL=1e-4, MAX_ITER, fem_params)
    opt = Opt(:LD_MMA, fem_params.np)
    opt.lower_bounds = 0
    opt.upper_bounds = 1
    opt.xtol_rel = TOL
    opt.maxeval = MAX_ITER
    iteration_counter = 0 # <-- new
    iteration_solutions = Any[] # <-- new
    function objective_fn(p0, grad)
        iteration_counter += 1 # <-- new
        push!(iteration_solutions, p0) # <-- new 
        println("called from iteration $iteration_counter")
        return gf_p(p0, grad; r, β, η, fem_params)
    end
    opt.max_objective = objective_fn
    (g_opt, p_opt, ret) = optimize(opt, p_init)
    println("got $g_opt at $p_opt after $numevals iterations (returned $ret)")
    return g_opt, p_opt
end

```

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<div class="post-metadata">

**Author:** ![mary](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mary/32/53139_2.png) [@mary](https://discourse.julialang.org/u/mary)\
**Post date:** [May 8, 2024, 8:41pm UTC](https://discourse.julialang.org/t/how-to-get-evaluation-number-in-optimization-using-nlopt/114020/3 "2024-05-08T20:41:42Z")

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HI @odow  
Thank you for your great response. That should work for saving the design (p0) at each iteration.  
do you have any idea that how can I use iteration\_counter in gf\_pf function and use that to generate random number in each iteration?

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<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [May 8, 2024, 8:43pm UTC](https://discourse.julialang.org/t/how-to-get-evaluation-number-in-optimization-using-nlopt/114020/4 "2024-05-08T20:43:43Z")

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You could pass `iteartion_counter` through to your `gf_p` function.

But you should be _very_ careful. Do you really want to use a random number? NLopt assumes your function is deterministic, that is, it will return the same objective value for repeated calls with the same value of `p0`.

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<div class="post-metadata">

**Author:** ![mary](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mary/32/53139_2.png) [@mary](https://discourse.julialang.org/u/mary)\
**Post date:** [May 8, 2024, 8:44pm UTC](https://discourse.julialang.org/t/how-to-get-evaluation-number-in-optimization-using-nlopt/114020/5 "2024-05-08T20:44:09Z")

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I want to use iteration\_counter in random.seed!(iteration\_counter) to generate number

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<div class="post-metadata">

**Author:** ![mary](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mary/32/53139_2.png) [@mary](https://discourse.julialang.org/u/mary)\
**Post date:** [May 8, 2024, 8:46pm UTC](https://discourse.julialang.org/t/how-to-get-evaluation-number-in-optimization-using-nlopt/114020/6 "2024-05-08T20:46:12Z")

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I want to do stochastic optimization. I am assuming uncertainty in material properties. therefore, at each iteration it should calculate material properties, find the solution and gradients.

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<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [May 8, 2024, 8:53pm UTC](https://discourse.julialang.org/t/how-to-get-evaluation-number-in-optimization-using-nlopt/114020/7 "2024-05-08T20:53:49Z")

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You should reconsider how you intend to optimize your parameters. Using NLopt with a stochastic function is likely not what you need.

You might want to:

- Use some sort of sample average approximation, where you optimize across a sample of different random values (say, 10 or 20) at each iteration, but where you keep the random values constant between iterations.
- Use some sort of algorithm that supports noisy objective functions, such as Bayesian Optimization.

Other people on this forum might chime in with some other ideas.

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**Author:** ![stevengj](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/stevengj/32/71_2.png) [@stevengj](https://discourse.julialang.org/u/stevengj)\
**Post date:** [May 8, 2024, 10:40pm UTC](https://discourse.julialang.org/t/how-to-get-evaluation-number-in-optimization-using-nlopt/114020/8 "2024-05-08T22:40:05Z")

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> [@odow](#):
>
> Use some sort of algorithm that supports noisy objective functions, such as Bayesian Optimization.

For local optimization of expected values of noisy objectives, machine-learning people like the Adam algorithm and its cousins. Several of these stochastic gradient-descent types of algorithms can be found [in Optimization.jl](https://docs.sciml.ai/Optimization/stable/optimization_packages/optimisers/) for example.

> [@mary](#):
>
> I want to do stochastic optimization. I am assuming uncertainty in material properties.

Uncertainty does not necessarily imply stochastic optimization; often, if you have uncertainty, you want to do some form of [robust optimization](https://en.wikipedia.org/wiki/Robust_optimization), for which there are a wide variety of algorithms.