I am currently discovering the world of
JuMP.jl and I have to say, I am amazed, thanks for this package!
TL;DR: how do I add constraints or limits to parameters which are not defined by
For some optimisation problems, I have to rerun the optimisation with different initial values, which is of course a common task. In order to save time (I have to be fast since the code is analysing realtime events), I would like to keep the model as it is and just change the initial values.
Just to have an explicit example (this is not an MWE, it’s just to demonstrate my workflow):
model = Model(with_optimizer(Ipopt.Optimizer)) register(model, :qfunc, 5, qfunc, autodiff=true) @variable(model, -1000 <= d_closest <= 1000, start=0.0) @variable(model, -10000 <= t_closest <= 10000, start=-400) @variable(model, -1000 <= z_closest <= 1000, start=476) @variable(model, -1 <= dir_z <= 1, start=0.2) @variable(model, -1000 <= t₀ <= 1000, start=0) optimize!(model)
Works perfectly fine for some - in the example hardcoded - starting values (they are later obtained by some topology checks beforehand).
I thought I can use
set_value() to reset the initial values and call
optimize!() again but there is no method for that type:
> set_value(d_closest, 10) MethodError: no method matching set_value(::VariableRef, ::Int64) Closest candidates are: set_value(!Matched::NonlinearParameter, ::Number) at /home/tgal/.julia/packages/JuMP/jnmGG/src/nlp.jl:144
Fair enough, the hint pointed me to create a
NonlinearParameter, which can be constructed using
So far so good:
@NLparameter(model, d_closest == 0.0) ... ... set_value(d_closest, 20) # works
However, I was not able to figure out how to keep the boundary conditions.
I naively tried this one but it’s obviously not the way to do it:
@NLparameter(model, d_closest == 0.0) @NLconstraint(model, cons1, -1000 <= d_closest <= 1000)
since later when running
optimize!(model), I get
IPOPT: Failed to construct problem.