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I read the corresponding paper and wanted to compare the results from table 1 on my machine to an algorithm that I implemented by myself. However, as with most distributed algorithms, the performance is highly depending on the hyper parameters. In the paper you described that you had a routine to tune the best parameter sets. Therefore I wanted to ask whether you would mind to share these optimal parameter sets for each grid and algorithm after running through this routine for the model type ACRPowerFlow.
Thanks a lot in advance!
The text was updated successfully, but these errors were encountered:
Sure! Please see below the link for the tuning that I used. It has two files: a main file that runs the test cases from PGLib, and a fine contains the tuning function. It has been a long time since I ran the code, but it should work.
Dear @mkhraijah
thanks a lot for providing this package!
I read the corresponding paper and wanted to compare the results from table 1 on my machine to an algorithm that I implemented by myself. However, as with most distributed algorithms, the performance is highly depending on the hyper parameters. In the paper you described that you had a routine to tune the best parameter sets. Therefore I wanted to ask whether you would mind to share these optimal parameter sets for each grid and algorithm after running through this routine for the model type
ACRPowerFlow
.Thanks a lot in advance!
The text was updated successfully, but these errors were encountered: