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Hi all,</div>
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I'm looking for some advice on how to implement a particular solving strategy.</div>
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I have a problem where there are two set of (binary) variables. One set, call it Q, has a quadratic number of variables, the other set,</div>
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call it E, has an exponential number of variables. The variables in E are actually products of those in Q and only variables in E</div>
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have non-zero objective coefficient. In my current strategy all Q variables and a reasonably small subset of E variables are created initially and</div>
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further E variables are priced-in later (if necessary).</div>
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In the sort of problems I'm aiming to solve, the user may well want to impose constraints on the variables in Q. I would like to use these constraints to</div>
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speed up the creation of the initial set of E variables. I'm thinking of an approach like this:</div>
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<div>Create Q variables</div>
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<div>Read in user constraints on the Q variables, if any</div>
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<div>Propagate these constraints (perhaps with several rounds) and (hopefully) produce fixings for some of the Q variables</div>
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<div>Use any fixings to speed up the creation of initial E variables. (They will also be used, of course, in any future pricing.)</div>
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I'm not sure how best to implement this. I could call presolve after step 2 but perhaps SCIP will just solve the problem at that point unless I somehow flag</div>
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that some E variables are on their way. I think I am looking to do propagations at step 3 and nothing else.</div>
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James</div>
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James Cussens</div>
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Room MVB 3.26</div>
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School of Computer Science, University of Bristol</div>
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Phone: +44 (0)117 455 8723</div>
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<a href="https://jcussens.github.io/">https://jcussens.github.io/</a><br>
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