sciml / autooptimize.jl Goto Github PK

We should run the analyses in tasks so that when the tasks hit a certain time limit they reject the analysis assuming it will fail.

This is probably a dumb question, but is something like this auto_optimize-able?

function odefunc(du,u,p,t)
    du[:] .= Eh(t).*u
end

prob = ODEProblem(odefunc,[0.1,0.2],(0.0,10.0))

I only know the function Eh(t) numerically (from a previous ODE solve).

What would be the best strategy for finding the right estimate of time to Hyper AutoOptimize? Should we perform a single step and benchmark the time taken and use it as a quantity for what is best?
Also would the greedy approach do? For eg we optimize weather we want to use autodiff or not and weather we want to use GPU or not. We can find this out in 2 ways -

1. Check if it is faster with AD or not with GPU turned off. Then if it is faster with AD, we try GPU with AD. For n parameters, we try 2n configurations
2. We check every configuration i.e. 2^n

Hello,

This may be coming from my ignorance (not a programmer first) but ] add AutoOptimize cannot find the package to install. I know the package is experimental; are we suppose to try to install it in any other way?

    Updating registry at `C:\Users\hhagh\.julia\registries\General`
    Updating git-repo `https://github.com/JuliaRegistries/General.git`
ERROR: The following package names could not be resolved:
 * AutoOptimize (not found in project, manifest or registry)

https://github.com/oxinabox/AutoPreallocation.jl