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It would be good to get the local optimums together with the "global". Is this already possible? If yes how can I do it?
In some cases, one might want to choose a local optimum that is, in some sense, "more robust" than the global one. For this reason having a list of local optimums as well would be useful
The text was updated successfully, but these errors were encountered:
That's interesting.
We do not have a specific function for that, but you can determine whether the optimizer has reached a local optimum (that is, whether the optimization has converged) by .should_stop() function. https://github.com/CyberAgentAILab/cmaes/blob/main/examples/ipop_cmaes.py#L40
I guess that it is possible to use the above criteria to create a list of local optimums.
It would be good to get the local optimums together with the "global". Is this already possible? If yes how can I do it?
In some cases, one might want to choose a local optimum that is, in some sense, "more robust" than the global one. For this reason having a list of local optimums as well would be useful
The text was updated successfully, but these errors were encountered: