# Passing "dynamic" argument to function in Optim.jl

**URL:** <https://discourse.julialang.org/t/passing-dynamic-argument-to-function-in-optim-jl/69202>\
**Category:** Optimization (Mathematical)\
**Tags:** optimization\
**Created:** [October 4, 2021, 6:48pm UTC](https://discourse.julialang.org/t/passing-dynamic-argument-to-function-in-optim-jl/69202 "2021-10-04T18:48:26Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![Gabriel\_Kreindler](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/gabriel_kreindler/32/24961_2.png) [@Gabriel\_Kreindler](https://discourse.julialang.org/u/Gabriel_Kreindler)\
**Post date:** [October 4, 2021, 6:48pm UTC](https://discourse.julialang.org/t/passing-dynamic-argument-to-function-in-optim-jl/69202/1 "2021-10-04T18:48:26Z")

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Hello,  
I have an objective function that solves for a vector `z` using an iterative algorithm. I then feed this into `optimize()`. I can use a “static” starting guess `z0` that I set before calling `optimize`. However, it might be faster to use the actual `z` obtained the last time `optimize` called the objective function. The idea is that often `optimize` is calculating numerical gradients so the new `z` will be very close.

I can probably achieve this using a global for `z0`, but I was wondering  
(A) whether this can be achieved more cleanly, and  
(B) if there are reasons to not do this altogether.

For this application, I can prove that `z` is unique (and hence the starting condition does not matter except for speed).

Here is a MWE (note, in the real application, I cannot solve `z` analytically as is the case here, so I do need to iterate)

```julia
function mylossfn(delta, xgrid, z0, z_data)

	z = z0
	error = 1.0

	# solve Bellman equation by iteration 
	# (in the real application there is no closed form soln)
	while error > 1e-8
		z_next = (xgrid .^ 2) .+ delta .* z

		error = sum( (z .- z_next).^2 )
	end

	# loss function
	return sum( (z .- z_data).^2 )

end

xgrid = 1:100
myz0 = zeros(100)
# read my_z_data from a data file
delta0 = 0.5

optimize((x -> mylossfn(x, myz0, my_z_data)),
		delta0,
		BFGS())

```

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<div class="post-metadata">

**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:** [October 4, 2021, 7:07pm UTC](https://discourse.julialang.org/t/passing-dynamic-argument-to-function-in-optim-jl/69202/2 "2021-10-04T19:07:09Z")

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> [@Gabriel\_Kreindler](#):
>
> I can probably achieve this using a global for `z0` , but I was wondering

No need for globals. Just mutate the `z0` argument in your `mylossfunction`, and then lexical scoping will ensure that `optimize((x -> mylossfunction(x, myz0, my_z_data)), ...)` uses whatever variable `myz0` you defined in your local scope.

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<div class="post-metadata">

**Author:** ![Gabriel\_Kreindler](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/gabriel_kreindler/32/24961_2.png) [@Gabriel\_Kreindler](https://discourse.julialang.org/u/Gabriel_Kreindler)\
**Post date:** [October 4, 2021, 8:16pm UTC](https://discourse.julialang.org/t/passing-dynamic-argument-to-function-in-optim-jl/69202/3 "2021-10-04T20:16:08Z")

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Fantastic, this is so helpful, thank you!

It seems to me that the way to do this (inside `mylossfn`) is

```julia
for i=1:length(z)
	z0[i] = z[i]
end

```

rather than the naive `z0=z` that I tried initially.