<div dir="ltr">Dear James,<div><br></div><div>This is exactly what I was looking for. Will try my luck with this one.</div><div>Hopefully it does not take too long to implement, but we know how that always goes.</div><div><br></div><div>Many thanks,</div><div>Aiman</div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Fri, Jun 10, 2022 at 4:06 PM James Cussens <<a href="mailto:james.cussens@bristol.ac.uk">james.cussens@bristol.ac.uk</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
<div dir="ltr">
<div style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">
<span style="color:black;font-size:12pt;font-family:Calibri,Arial,Helvetica,sans-serif">Dear Aiman,</span>
<div style="color:black;font-size:12pt;font-family:Calibri,Arial,Helvetica,sans-serif">
<br>
</div>
<div style="color:black;font-size:12pt;font-family:Calibri,Arial,Helvetica,sans-serif">
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" rel="noopener noreferrer" target="_blank">
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>
<div style="color:black;font-size:12pt;font-family:Calibri,Arial,Helvetica,sans-serif">
<br>
</div>
<div style="color:black;font-size:12pt;font-family:Calibri,Arial,Helvetica,sans-serif">
James</div>
<br>
</div>
<div>
<div style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">
<br>
</div>
<div id="gmail-m_-6811732197467023630Signature">
<div>
<div style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">
</div>
<div style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">
James Cussens</div>
<div style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">
Room MVB 3.26<br>
</div>
<div style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">
Dept of Computer Science, University of Bristol</div>
<div style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">
<a href="https://jcussens.github.io/" target="_blank">https://jcussens.github.io/</a></div>
<div style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">
<span style="font-size:11.5pt">Funded PhDs available in Bristol in the following areas: <a href="http://www.bristol.ac.uk/cdt/compass/" rel="noopener noreferrer" target="_blank"><span>Data Science</span></a>, <a href="http://www.bristol.ac.uk/cdt/interactive-ai/" rel="noopener noreferrer" target="_blank"><span>Interactive
AI</span></a>, <a href="http://www.bristol.ac.uk/cdt/cyber-security/" rel="noopener noreferrer" target="_blank"><span>Cyber Security</span></a> or <a href="http://www.bristol.ac.uk/cdt/digital-health/" rel="noopener noreferrer" target="_blank"><span>Digital
Health</span></a>. </span><br>
</div>
</div>
</div>
</div>
<div id="gmail-m_-6811732197467023630appendonsend"></div>
<hr style="display:inline-block;width:98%">
<div id="gmail-m_-6811732197467023630divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> Scip <<a href="mailto:scip-bounces@zib.de" target="_blank">scip-bounces@zib.de</a>> on behalf of aiman social <<a href="mailto:aimansocialacc@gmail.com" target="_blank">aimansocialacc@gmail.com</a>><br>
<b>Sent:</b> 10 June 2022 04:25<br>
<b>To:</b> <a href="mailto:scip@zib.de" target="_blank">scip@zib.de</a> <<a href="mailto:scip@zib.de" target="_blank">scip@zib.de</a>><br>
<b>Subject:</b> [SCIP] Strategies for Tuning Parameters in MILP problems & Pyomo compatibility</font>
<div> </div>
</div>
<div>
<div dir="ltr">Dear SCIP team,<br>
<br>
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>
<br>
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>
<br>
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>
<ol>
<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>
<br>
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>
<br>
Any help would be much appreciated. <br>
<br>
Regards,<br>
Aiman Nazmi<br>
</div>
</div>
</div>
</blockquote></div>