Question: tron vs. trunk for unconstrained problems

Hi JSO team,

Do you have any guidance about which of these solvers is "better" for unconstrained problems, and, if so, why? I had naively assumed that a solver specifically designed for unconstrained problems (`trunk`) would be better, but in some applications where I have compared the two, `tron` appears to converge more quickly.

If it's useful, my current applications are related to maximum likelihood estimation problems, using a multinomial logit log-likelihood function. I am providing analytic gradients, and I'm using ForwardDiff to turn those gradients into a hessian-vector product for the `nlp` specification. In some of these applications, the objective is strictly convex, but that is not always so. It is often the nonconvex situations where `tron` appears to converge more quickly.

Thanks in advance for any advice you may have!