I'm trying to code an optimization problem in Julia using JuMP. The objective function has two matrix multiplications.
First, multiply the vector of w with size (10) by the matrix of arr_C with size (20, 10). So the w should be transposed to size (1, 10) to perform matrix multiplication.
Second, multiply the vector of w_each_sim with size (20) by the result of the first multiplication, which is also a vector of size (20). So the multiplication should be like (1x20) x (20x1) to achieve a scalar. Please read until the last line of the question because I applied updates according to suggestions.
My first try is as follows:
julia> using JuMP, Ipopt
julia> a = rand(20, 10);
julia> b = rand(20); b = b./sum(b)
julia> function port_opt(
n_assets::Int8,
arr_C::Matrix{Float64},
w_each_sim::Vector{Float64})
"""
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
"""
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, w_each_sim * log10.([w[i]*arr_C[i] for i in 1:n_assets]))
optimize!(model)
@show value.(w)
return value.(w)
end
julia> port_opt(Int8(10), a, b)
ERROR: UndefVarError: i not defined
Stacktrace:
[1] macro expansion
@ C:\Users\JL\.julia\packages\JuMP\Z1pVn\src\macros.jl:1834 [inlined]
[2] port_opt(n_assets::Int8, arr_C::Matrix{Float64}, w_each_sim::Vector{Float64})
@ Main e:\MyWork\paperbase.jl:237
[3] top-level scope
@ REPL[4]:1
The problem is with the @NLconstraint line.
How can I make this code work and get the optimization done?
Aditional tests
As @Shayan suggested, I rectified the objective function as follows:
function Obj(w, arr_C, w_each_sim)
first_expr = w'*arr_C'
second_expr = map(first_expr) do x
log10(x)
end
return w_each_sim * second_expr
end
function port_opt(
n_assets::Int8,
arr_C::Matrix{Float64},
w_each_sim::Vector{Float64})
....
....
@NLconstraint(model, sum(w[i] for i in 1:n_assets) == 1)
@NLobjective(model, Max, Obj(w, arr_C, w_each_sim))
optimize!(model)
@show value.(w)
return value.(w)
end
a, b = rand(20, 10), rand(20); b = b./sum(b);
port_opt(Int8(10), a, b)
# Threw this:
ERROR: Unexpected array VariableRef[w[1], w[2], w[3], w[4], w[5], w[6], w[7], w[8], w[9], w[10]] in nonlinear expression. Nonlinear expressions may contain only scalar expressions.
Now, based on @PrzemyslawSzufel's suggestions, I tried this:
function Obj(w, arr_C::Matrix{T}, w_each_sim::Vector{T}) where {T<:Real}
first_expr = zeros(T, length(w_each_sim))
for i∈size(w_each_sim, 1), j∈eachindex(w)
first_expr[i] += w[j]*arr_C[i, j]
end
second_expr = map(first_expr) do x
log(x)
end
res = 0
for idx∈eachindex(w_each_sim)
res += w_each_sim[idx]*second_expr[idx]
end
return res
end
function port_opt(
n_assets::Int8,
arr_C::Matrix{Float64},
w_each_sim::Vector{Float64})
model = Model()
@variable(model, 0<= w[1:n_assets] <=1)
@NLconstraint(model, +(w...) == 1)
register(model, :Obj, Int64(n_assets), Obj, autodiff=true)
@NLobjective(model, Max, Obj(w, arr_C, w_each_sim))
optimize!(model)
@show value.(w)
return value.(w)
end
a, b = rand(20, 10), rand(20); b = b./sum(b);
port_opt(Int8(10), a, b)
# threw this
ERROR: Unable to register the function :Obj because it does not support differentiation via ForwardDiff.
Common reasons for this include:
* the function assumes `Float64` will be passed as input, it must work for any
generic `Real` type.
* the function allocates temporary storage using `zeros(3)` or similar. This
defaults to `Float64`, so use `zeros(T, 3)` instead.
