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CandidateDYCORS implements which strategy? #12
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DYCORS implements DYCORS-LMSRBF. The idea of perturbing fewer and fewer coordinates comes from DDS, but I agree it is confusing. I'll clarify the documentation. |
I have two more questions: When set to Is the SO-MI ([1]) algorithm implemented in pySOT? If yes, how to use it? Thanks [1] Juliane Mueller, Christine A Shoemaker, and R Piche. SO-MI: A Surrogate Model Algorithm for Computationally Expensive Nonlinear Mixed-Integer, Black-Box Global Optimization Problems. Computers and Operations Research, 40(5):1383–1400, 2013. |
That's correct. There are also methods for perturbing the continuous or integer variables separately. It should be possible to do something similar to SO-MI by cycling the adaptive sampling methods that are used in the paper. I believe they are all available in pySOT? |
Yes! From the documentation:
Which is what SO-MI is doing i.e. generate 4 groups of candidate points, and evaluate the best point from each of the groups. So to use the SO-MI algorithm from the paper, we only need to define the Thanks a lot, now it's all clear 👍 Edit: Just for reference: this is not all what SO-MI is doing. Besides generating 4 groups of candidate points, the perturbations are also done differently. From the paper [1]:
Here k is the number of continuous variables. Thus I would say for now the SO-MI algorithm is not implemented in pySOT. Although with just a few changes one can code SO-MI like algorithm using the current version of pySOT. |
Great! :) |
I missed the edit you made and I'll look into how to add support for perturbations of different sizes. |
I'm closing this and I'll open a new enhancement issue for implementing SO-MI. |
In the documentation it says:
however after reading the code and the DYCORS paper (Regis & Shoemaker, 2012), the search strategy it's actually DYCORS-LMSRBF, not DYCORS-DDSRBF.
Please confirm.
Thanks
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