2

I am trying to define a @NLexpression in JuMP that has different specifications at different indices. In the below example, I want the expression y[j,k] to be defined as 1/(x[j] - x[k]) when j != k and take some other value when j == k. I can simulate this behavior by defining an auxiliary variable z and adding constraints conditional on index values. Is there a similar way to define expression conditional on index values?

using JuMP, Ipopt

model = JuMP.Model(with_optimizer(Ipopt.Optimizer))

@variable(model, 0 <= x[1:2])
@NLexpression(model, y[j=1:2,k=1:2], 1/(x[j] - x[k])) # <- problematic line

@variable(model, z[1:2,1:2])
for j=1:2, k=1:2
    if j == k
        @constraint(model, z[j,k] == 1)
    else
        @NLconstraint(model, z[j,k] == 1/(p[j] - p[k]))
    end
end

display(model)
hilberts_drinking_problem
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1 Answers1

3

You're not obligated to use JuMP's macros to create containers for expressions. You should be able to conditionally create the expressions as follows:

model = JuMP.Model()

@variable(model, 0 <= x[1:2])
y = Dict()  # Or Array, if you prefer.
for j=1:2, k=1:2
    if j == k
        y[j,k] = 1
    else
        y[j,k] = @NLexpression(model, 1/(p[j] - p[k]))
    end
end
mlubin
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