How to get the extreme rays of a model, please

[zhx-11]

If a model has already been established, and during the solution process it is found that the model is solvable but the objective function is unbounded, what methods can be used to obtain its extreme rays? And how to calculate the values of the decision variables corresponding to the extreme rays?

My code is as follows：

using JuMP
using Gurobi

model = Model(Gurobi.Optimizer)
set_optimizer_attribute(model, "OutputFlag", 0)
@variable(model, x[1:4]>=0)
@variable(model, y[1:5]>=0)
@constraint(model, [i in 1:4,j in 1:5], i*x[i]+j*y[j]>=i*j)
@objective(model, Max, sum(x[i] for i in 1:4)+sum(y[j] for j in 1:5 ) )

optimize!(model)

Of course, you can also give a simple example of how to get it。
If there is no direct method to obtain the extreme rays, please also give me some feedback, thank you

[blegat]

Hi @zhx-11. In the future, I recommend posting in https://discourse.julialang.org/c/domain/opt/13, you'll get a faster answer.
The details are here: https://jump.dev/JuMP.jl/stable/moi/background/infeasibility_certificates/#Unbounded-problems
If primal_status(model) returns INFEASIBILITY_CERTIFICATE then value.(x) and value.(y) give you the value of x and y in the extreme ray.