Thanks a lot. This helps very much. ... View more

Thank you for your careful response.   1. The bug leaves us in a hard place with a time constraint. Because most other parts of the data work in this project are in SAS, a SAS solution would be best for us. We tried PROC NLP but for large sample sizes it doesn't seem to work. Is there another candidate solution within SAS? Or is there something outside of SAS that you would recommend?   2. For this objective function we have the exact 8x1 gradient vector and the exact 8x8 Hessian (computed using a symbolic algebra package called MAXIMA). But there doesn't seem to be any way to use that. We had assumed that in hill-climbing type grid search, having those exact values would help improve search efficiency. But we can't figure out any way to teach PROC OPTMODEL what we have, let alone make it use that information. Is there some procedure in SAS for this type of problem, that will that exact gradient and Hessian information?   3. Srini has noted the strange behavior of the BNDRANGE option. Increasing the bound reduces the number of local optima identified. If that is anomalous, would you consider that a bug as well?   Please help. Thank you. Best, Murgie ... View more

I want to add to my coauthor Srini's question above. From https://support.sas.com/resources/papers/proceedings13/158-2013.pdf we find   "RECURSION The uniform sampling, starting-point selection, local optimization, and sample-point update steps are repeated in sequence until either no sampled points are selected or the maximum number of local optimizations (specified via the MSMAXSTARTS= parameter) has been completed. At termination the algorithm reports the best local minimum that it has found."   1. Does this mean that any set of initial values given is over-ridden by SAS when we invoke the switch "ms." 2. This note above refers to "msMAXstarts" while Srini has used "msNUMstarts." Does that make any difference, or explain anything? 3. If starting values are generated by SAS on its own, with the uniform random number generator, and if subsequent starting points depend on initial (randomly selected) starting points, it leads to the scary instability of the same data, code, and machine, leading to different estimates on subsequent runs. Is there some way of overcoming this? 4. Must we just keep increasing the value of the parameter in msnumstarts or msmaxstarts till successive runs yield the same answer? This is not a previously solved problem, so there is no easy way to tell a priori if msnumstarts or msmaxstarts = 100 or 1000 is large enough. 5. Is there some way to make SAS take a list of starting points we give it, and use those in PROC OPTMODEL? Our reading is that whatever init values are specified are simply ignored. If they are used, and they don't change from one run to the next, getting different estimates each time is a huge drawback. Please help. Thank you. ... View more