# DiffEqFlux / diffeq\_adjoint: no method matching back!(::Float64)

**URL:** https://discourse.julialang.org/t/diffeqflux-diffeq-adjoint-no-method-matching-back-float64/24648
**Category:** Machine Learning
**Tags:** diffeq
**Created:** [May 27, 2019, 12:23pm UTC](https://discourse.julialang.org/t/diffeqflux-diffeq-adjoint-no-method-matching-back-float64/24648 "2019-05-27T12:23:29Z")
**Posts on this page:** 1
**Showing post:** 4

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### Author: ![Tamas\_Papp](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tamas_papp/32/25949_2.png) [@Tamas\_Papp](https://discourse.julialang.org/u/Tamas_Papp)
#### Post date: [May 28, 2019, 1:36pm UTC](https://discourse.julialang.org/t/diffeqflux-diffeq-adjoint-no-method-matching-back-float64/24648/4 "2019-05-28T13:36:18Z")

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> [@mihai](#):
>
> Any ideas what (global) optimizers are well-suited for my case, especially when fitting noisy data?

There was a related discussion a while ago:

> [@Global optimization: Simulated Method of Moments](https://discourse.julialang.org/t/global-optimization-simulated-method-of-moments/23844):
>
> I work with non-linear models that need to be calibrated to match data moments. The setup is simple. I have a function that takes a set of parameters as input (for example, a vector of floats), solves the model, and returns a measure of the distance between the model-generated moments and the data moments. Typically there are more moments than parameters. Hence, I use some simple weighting function to summarize the differences between model and data moments into one float. The objective is to m…

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