Modifying solver parameters in JuMP after building the model

Specific Domains Optimization (Mathematical)
1

I was working on a heuristic where I modify an ILP within a loop and solve it for a certain iterations. As of now, within the loop, I build the model repeatedly during each iteration which is costing me more time. Is there a possibility to not repeatedly build the model while modifying the model and decrementing the timelimit to solve it within the loop? A simple pseudocode below intended to illustrate what I’m trying to do.

f = 10

for i in 1:10

    m = build_ILP(instance, parameters)
    @constraint(m.model, x + y <= 10*f)
    solve(m.model)
    f -= 1

end
2

You can modify a JuMP model by adding additional constraints: just call @constraint again. Similarly, to modify the time limit, just call set_time_limit_sec again.

Option 1: look up the variables in m.model:

m = build_ILP(instance, parameters)
model = m.model
x = model[:x]
y = model[:y]
f = 1
for i in 1:10
    set_time_limit_sec(model, 10 - i)
    @constraint(mmodel, x + y <= 10 * f)
    optimize!(model)
    f -= 1
end

Option 2: return the variables from the build_ILP function:

model, x, y = build_ILP(instance, parameters)
for f in 10:-1:1
    set_time_limit_sec(model, f)
    @constraint(mmodel, x + y <= 10 * f)
    optimize!(model)
end

Here’s a Bender decomposition example: Benders Decomposition · JuMP

1 Like
3

Thanks a lot @odow. But since some of my previous implementation is still using JuMP v0.18, could you please let me know how to replicate this in JuMP v0.18?

4

Please update to 0.21. There is no reason to stay on 0.18.