JuMP: Register arbitary number of functions and use as constraints

[Marc.Cox]

I was just dealing with a similar issue like “… metaprogramming somehow. But (NOT) using eval() …”
And the crux of the solution came from @ChrisRackauckas and @c42f who suggested RuntimeGeneratedFunction.jl .

A Simple MWE code for RuntimeGeneratedFunction.jl here >>

Setting ApproxFun Space , tried several tricks but still receive **ERROR: LoadError: Cannot infer spaces** – specifically when using GeneralizedGenerated.jl for Generalized generated functions

Thank you All for your input and insights ! You motivated me to push on until we found an answer together ! Ok so I’d like to take a swing at recapping what we’ve learned so far here … ### # Template solution motivated by @Chris_Foster and @ChrisRackauckas Thanks Chrisss^2 ! # .. Marc BEGIN WORKS NOW !! julia> func_f161 = @RuntimeGeneratedFunction(Meta.parse("f16(x) = x")) # Julia response @RuntimeGeneratedFunction( >> RuntimeGeneratedFunction(#=in Main=#, :((x,)->begin #= no…

More below :

# WORKS WAS -- f_symbolic_ans(x) = (x^3-x^2+1)
str_f_symbolic_BigD = "f_symbolic_ans(x) = (x^3-x^2+1)"
func_f_symbolic_BigD = @RuntimeGeneratedFunction(Meta.parse(str_f_symbolic_BigD))
#
f371 = ApproxFun.Fun(x->func_f_symbolic_BigD(x),Domain(r_left_x..r_right_x))

Also figured out my answer to "… quickly handling / removing Inf from answers "
via

# Base.Generator{  
gen_iter_f2 = ( r_x for r_x in Iterators.filter( x -> ( isfinite(ApproxFun.extrapolate(f371,x)) & isfinite(ApproxFun.extrapolate(funjac_u1,x)) )
                ,range_x ) )

…

However @Gaussia if you have “… figured out a better approach for achieving what I wanted, without using Jump. …”
I’d love to know what it was, could you outline a sketch in pseudo-code or such ?