Hello, I am looking for help in terms of improving the efficiency/runtime of Proc Optmodel. Here's some background on my problem: It's specifically optimizing selection. I have 200,000 rows with the variable being a Binary Include flag. The subset I want "included" has to meet various constraints in the aggregate - weighted averages, sums, etc. I am using the Mixed Integer Linear solver, as that is the one that works given I have a Binary variable. Issue is that once I add a key constraint, the runtime has increased from 20 seconds to above 4 minutes. I know that this key constraint is "limiting", ie the result is right at my constraint boundary. Otherwise, it is very similar to other constraints I have that do not seem to slow down runtime. I know optmodel has tons of options, but I'm just not sure which could help me. I did cut the runtime in half by setting Presolver=aggressive, but I'm weary about what this actually does to the data. I get the following in log: "WARNING: Removed 17 constraint coefficients whose absolute values are not greater than 1E-9." This confuses me because none of the hard coded values I have in there are < 1E-9, but maybe I need to better understand what all is included in a constraint coefficient. I'm nopt using any other options on the solve line. Let me know if anything else will help describe my problem. Any suggestions are greatly appreciated! Thanks
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