Comments (9)
If we have a KnitroSolver, then we could store
kc
in it, and write a finalizer. That would allow users to perform solves of the same problem from a different starting point, etc.
Great, I will make it concrete in a PR.
from nlpmodelsknitro.jl.
Could you add a link to the documentation describing how to retrieve those values?
from nlpmodelsknitro.jl.
Here is the link to the documentation Reading model/solution properties
There is no further doc for KNITRO.jl, but the useful functions can be found in src/kn_attributes.jl.
from nlpmodelsknitro.jl.
Could we return the kc
object in stats
so the user can query those if they want? I don’t think we should store entire Hessians or Jacobians in stats
...
from nlpmodelsknitro.jl.
Agreed. I think we can pass the kc
object, the only drawback is that the user has to use KNITRO.KN_free(kc)
(which is done right now at the end of the knitro
call) to "finalize" the model similar to a CUTEstModel.
Another alternative would use kwargs so that the user asks from the solver call for the returned values.
from nlpmodelsknitro.jl.
Yes, that’s better. Let’s make it optional. But does that mean we need a KnitroSolver to preallocate?
from nlpmodelsknitro.jl.
Yes, a KnitroSolver would be a clean way to do it. So, the user needs to specify the evals from the initialization of the KnitroSolver.
from nlpmodelsknitro.jl.
Yes. There could be a check in the solver that the space has been allocated (and an error if it hasn’t).
from nlpmodelsknitro.jl.
If we have a KnitroSolver, then we could store kc
in it, and write a finalizer. That would allow users to perform solves of the same problem from a different starting point, etc.
from nlpmodelsknitro.jl.
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from nlpmodelsknitro.jl.