Optimization using JuMP

Specific Domains Optimization (Mathematical)
5

Unfortunately, this isn’t true

That’ll teach me for replying without reading the question properly or testing the example. I assumed too much :laughing:

How about this:

using JuMP, Ipopt
a = rand(20, 10)
b = rand(20); b = b./sum(b)

"""
Calculate weight of each asset through optimization

Parameters
----------
    n_assets::Int8 - number of assets
    arr_C::Matrix{Float64} - array of C
    w_each_sim::Vector{Float64} - weights of each similar TW

Returns
-------
    w_opt::Vector{Float64} - weights of each asset
"""
function port_opt(
    n_assets::Int8,
    arr_C::Matrix{Float64},
    w_each_sim::Vector{Float64},
)
    model = Model(Ipopt.Optimizer)
    @variable(model, 0<= w[1:n_assets] <=1)
    @NLconstraint(model, sum(w[i] for i in 1:n_assets) == 1)
    @NLobjective(
        model,
        Max, 
        sum(
            w_each_sim[i] * log10(sum(w[j] * arr_C[i, j] for j in 1:size(arr_C, 2)))
            for i in 1:length(w_each_sim)
        )
    )
    optimize!(model)
    return value.(w)
end

port_opt(Int8(10), a, b)