How to speed up ipopt with HSL ma86 using parallel processing

[odow]

I thought this model looked familiar:

Struggle at large scale problem of nlp using ipopt

hi there, I have a non-linear problem where the decision variable is a matrix of dimensions m * n * k. The constraints may also be somewhat complex. When I set m to a small subset of the dataset, e.g., 10, the program seems to work normally. However, when I set m to a slightly larger value, e.g., 50, the program does not produce any output, even though I set print_level to 12. There is no output at all. I am a bit confused and don’t know how to debug this. Is the program running normally (even t…

Your build issue is constraints like this:

for f in F
    if f == "NULL"
        continue
    end
    B = sum([decision_var[f, r, i, t, d] * matrix_1[r, i, t, d] * matrix_2[r, i, t, d, f] for (r, i, t, d) in product(R, I, T, D)])
    A = sum([decision_var[f, r, i, t, d] * matrix_1[r, i, t, d] * matrix_2[r, i, t, d, f] * matrix_3[r, i, t, d, f] for (r, i, t, d) in product(R, I, T, D)])
    C = sum([decision_var[f, r, i, t, d] * matrix_1[r, i, t, d] * matrix_2[r, i, t, d, f] * matrix_4[r, i, t, d, f] for (r, i, t, d) in product(R, I, T, D)])
    AB = A / B
    CB = C / B
    expr = AB * (1 - CB) - CB
    @constraint(model, P_lower[f] <= expr <= P_upper[f])
end

JuMP doesn’t (currently) exploit repeated subexpressions, so even though you have only 5103 variables, there are 14,885,451 terms in the Hessian. That’s a lot!