As the title says, I would like some insight on what Julia Optimization package would be most appropriate to efficiently solve nonlinear problems with potentially exploitable sparsity structures (e.g. block diagonal Hessian).
I have come across various packages:
- NLPModels.jl emphasises the capability for user-defined jac/hess structure;
- JuMP.jl, Optim.jl, NLOpt.jl all seem to support nonlinear problems, but I’m not sure what happens under the hood for exploiting sparsity.
Perhaps someone with experience on this could help?
Many thanks in advance:)