How to use Flux for a general non-linear minimization

  1. I am trying to apply great features Flux for likelihood minimization problem.
    Using ML language, losses for all training data points are summed up,
    there is just a function LLH() to be minimized. Is there an easy hack to train! functions that can help me?
using Flux
using Flux.Tracker
#
const data = [randn() for _ in 1:1000]
μ = param(0.5)
σ = param(1.1)
model(x) = 1/(sqrt(2π)*σ)*exp(-(x-μ)^2/(2*σ^2))
LLH() = sum(-log.(model.(data)))
# grad also works
gs = Tracker.gradient(LLH, Params([μ,σ]))
gs[μ], gs[σ]
# now I need to have an infinite training loop until convergence
  1. I like very much NLopt minimizer with LD_LBFGS algorithm.
    Any suggestions on how to interface to it? (customary apply function?)

Many thanks.

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You can easily train a flux model using Optims LBFGS with the help of

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