[SCIP] Strategies for Tuning Parameters in MILP problems & Pyomo compatibility

[aiman social]

Dear SCIP team,

I am currently working on an MILP scheduling problem using SCIP (with
python Pyomo framework).
I’m hoping to get some clarity on the following questions (or if there are
docs for reference do let me know).

Using Pyomo framework, SCIP 6.0.0 does not seem to report back the final
“dualbound” and “primalbound”. Is this expected?
I'm currently reading the gap results based on the SCIP terminal print out.

I'm also looking to improve the current performance (time to gap) of the
model.
I’ve tried the preset changes suggested by SCIP (set emphasis feasibility
gives the best current time to gap), but would like to make more granular
changes.
For this we have a few questions:

   1. Are there any suggested strategies to employ in changing individual
   params?
   2. Should I tackle granular settings in a specific order? (Try presolve
   first, then heuristics, and later nodeselection etc.)
   3. Are automated parameter tuning methods available? (similar to
   Bayesian parameter tuning in ML)
   4. Are there other low hanging fruits I should try to improve
   performance before making granular changes?


Additionally, I've experienced that different hardware led to different
performance outcomes (the hardware had ample headroom in each test).
Is this behavior expected?

Any help would be much appreciated.

Regards,
Aiman Nazmi
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://listserv.zib.de/pipermail/scip/attachments/20220610/7d9bb4c4/attachment.html>