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<span style="color:black;font-size:12pt;font-family:Calibri,Arial,Helvetica,sans-serif">Dear Aiman,</span>
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Automatic parameter tuning methods are available in the sense that there is parameter tuning software that is agnostic as to what the target algorithm is. One such example is SMAC3
<a href="https://github.com/automl/SMAC3" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="0">
https://github.com/automl/SMAC3</a> . One just writes an appropriate wrapper, chooses some SCIP parameters to tune and a bunch of training instances (and set the objective to runtime). Perhaps someone has already done this - I would like to for my own SCIP
application, but I never find the time! </div>
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James</div>
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James Cussens</div>
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Room MVB 3.26<br>
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Dept of Computer Science, University of Bristol</div>
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<a href="https://jcussens.github.io/">https://jcussens.github.io/</a></div>
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<span style="font-size:11.5pt">Funded PhDs available in Bristol in the following areas: <a href="http://www.bristol.ac.uk/cdt/compass/" target="_blank" rel="noopener noreferrer"><span>Data Science</span></a>, <a href="http://www.bristol.ac.uk/cdt/interactive-ai/" target="_blank" rel="noopener noreferrer"><span>Interactive
AI</span></a>, <a href="http://www.bristol.ac.uk/cdt/cyber-security/" target="_blank" rel="noopener noreferrer"><span>Cyber Security</span></a> or <a href="http://www.bristol.ac.uk/cdt/digital-health/" target="_blank" rel="noopener noreferrer"><span>Digital
Health</span></a>. </span><br>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> Scip <scip-bounces@zib.de> on behalf of aiman social <aimansocialacc@gmail.com><br>
<b>Sent:</b> 10 June 2022 04:25<br>
<b>To:</b> scip@zib.de <scip@zib.de><br>
<b>Subject:</b> [SCIP] Strategies for Tuning Parameters in MILP problems & Pyomo compatibility</font>
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<div dir="ltr">Dear SCIP team,<br>
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I am currently working on an MILP scheduling problem using SCIP (with python Pyomo framework).<br>
I’m hoping to get some clarity on the following questions (or if there are docs for reference do let me know).<br>
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Using Pyomo framework, SCIP 6.0.0 does not seem to report back the final “dualbound” and “primalbound”. Is this expected?<br>
I'm currently reading the gap results based on the SCIP terminal print out.<br>
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I'm also looking to improve the current performance (time to gap) of the model.<br>
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.<br>
For this we have a few questions:<br>
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<li>Are there any suggested strategies to employ in changing individual params?</li><li>Should I tackle granular settings in a specific order? (Try presolve first, then heuristics, and later nodeselection etc.)</li><li>Are automated parameter tuning methods available? (similar to Bayesian parameter tuning in ML)</li><li>Are there other low hanging fruits I should try to improve performance before making granular changes?</li></ol>
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Additionally, I've experienced that different hardware led to different performance outcomes (the hardware had ample headroom in each test).<br>
Is this behavior expected?<br>
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Any help would be much appreciated. <br>
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Regards,<br>
Aiman Nazmi<br>
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