I want to use EAGO.jl as an alternative solver to ipopt for nonlinear problems. I ran the example from the website (GitHub - PSORLab/EAGO.jl: A development environment for robust and global optimization) and it works fine. When I slightly rewrite the problem using @NLexpressions, the problem does not work anymore. Running optimize!() breaks Julia and gives me:
This keeps going on and on, I have to force quit the editor. Any ideas what can be wrong are welcome.
The rewritten problem:
using JuMP, EAGO
m = Model(EAGO.Optimizer)
# Define bounded variables
xL = [10.0; 0.0; 0.0; 0.0; 0.0; 85.0; 90.0; 3.0; 1.2; 145.0]
xU = [2000.0; 16000.0; 120.0; 5000.0; 2000.0; 93.0; 95.0; 12.0; 4.0; 162.0]
@variable(m, xL[i] <= x[i=1:10] <= xU[i])
# Define nonlinear constraints
@NLconstraint(m, e1, -x[1]*(1.12+0.13167*x[8]-0.00667* (x[8])^2)+x[4] == 0.0)
@NLconstraint(m, e3, -0.001*x[4]*x[9]*x[6]/(98-x[6])+x[3] == 0.0)
###Original constraints
##@NLconstraint(m, e4, -(1.098*x[8]-0.038* (x[8])^2)-0.325*x[6]+x[7] == 57.425)
##@NLconstraint(m, e5, -(x[2]+x[5])/x[1]+x[8] == 0.0)
###Rewritten constraints
@NLexpressions(m, begin
ex1, -(x[2]+x[5])/x[1]+x[8]
ex2, -(1.098*x[8]-0.038* (x[8])^2)-0.325*x[6]+x[7]
end)
@NLconstraint(m, e4, ex2 == 57.425)
@NLconstraint(m, e5, ex1 == 0.0)
# Define linear constraints
@constraint(m, e2, -x[1]+1.22*x[4]-x[5] == 0.0)
@constraint(m, e6, x[9]+0.222*x[10] == 35.82)
@constraint(m, e7, -3*x[7]+x[10] == -133.0)
# Define nonlinear objective
@NLobjective(m, Max, 0.063*x[4]*x[7] - 5.04*x[1] - 0.035*x[2] - 10*x[3] - 3.36*x[5])
# Solve the optimization problem
JuMP.optimize!(m)
I should add that if I split the expressions into two separate @NLexpression, the problem persists, but if I do only one constraint as @NLexpression and the other as @NLconstraint, there are no issues.
