search

PSORLab / EAGO.jl

A development environment for robust and global optimization
MIT License
140 stars 16 forks source link

EAGO and new nonlinear JuMP #128

Open mzagorowska opened 1 month ago

mzagorowska commented 1 month ago

Not sure if I should have posted it as a bug or feature request. It would be great to make EAGO work with the new nonlinear syntax in JuMP (i.e. using @constraint instead of @NLconstraint). Briefly, this works:

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]/(98.0 - x[6]) + x[3] == 0.0)
@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)

# 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, -3.0*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)

but this doesn't:

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
@constraint(m, e1, -x[1]*(1.12 + 0.13167*x[8] - 0.00667*(x[8])^2) + x[4] == 0.0)
@constraint(m, e3, -0.001*x[4]*x[9]*x[6]/(98.0 - x[6]) + x[3] == 0.0)
@constraint(m, e4, -(1.098*x[8] - 0.038*(x[8])^2) - 0.325*x[6] + x[7] == 57.425)
@constraint(m, e5, -(x[2] + x[5])/x[1] + x[8] == 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, -3.0*x[7] + x[10] == -133.0)

# Define nonlinear objective
@objective(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)

and I get:

image

My st:

image

DimitriAlston commented 1 month ago

Thanks for mentioning this. We will look into updating EAGO to add this functionality.