I mean, in handling boxed constraints?
In terms of stability, and more importantly, the numerical performance?
I have already written some well-optimized and well-tested C/CUDA/C++ codes for several unconstrained optimzation methods.
And I use augmented largrangian to handle constraints, however, it looks like bounded BFGS is quite different than the standard BFGS, I cannot do it over my existing BFGS routine,it require almost a complete re-written, I dont really want to do that unless there is significant gain there.
Have anyone, with practical experiences, can tell me if the rewritten is worth doing or I can stick to the let Lagrangian to handle it?
Btw, I usually use BFGS to solve MLE type optimzation problems, where the evaluation of the objective function is costly.