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<p>Dear SCIP team,</p>
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<p>I am currently using SCIP as a branch-and-cut solver for an integer linear problem. Since the nature of the problem I want to solve using SCIP always stays the same, I want to optimize the configuration parameters to achieve better runtimes and wondered
whether You can give me some hints towards which settings are usually the most impactful, since I found the number of parameters to be very large. I am guessing that most likely some of the "branching/" parameters such as "branching/firstsbchild" are useful
in my case, but I am not sure.</p>
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<p>My problem consists of only binary decision variables and very systematic constraints. First, equally sized subsets of the variables have the constraint, that only one of them can be true, which I implemented as an SOS1 constraint. Secondly, every binary
variable either enables or disables a matrix, all of the matrices of the enables variables are then summed up and have to be less than a constraint matrix at every entry. The constraint matrix roughly allows for each SOS1 group to activate one matrix such
that the sum stays below it (e.g. 621 of 624 SOS1 constraints have an active members in my solutions so far). The objective is to maximize the sum of the entries in the summed up matrix.</p>
<p>The problem scales with the number of binary variables and the size of the matrices, all of which are of the same size.</p>
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<p>I hope it is possible for you to take a look at my problem and maybe propose some settings.<br>
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<p>Kind regards,</p>
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<p>Nils Speetzen</p>
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<p>p.s.: I am using SCIP as a library for C++, the runtime for a reference problem is currently about 20 seconds long.<br>
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