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dme65 / pySOT

Surrogate Optimization Toolbox for Python
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Using known objective function values #16

Closed rho80 closed 7 years ago

rho80 commented 7 years ago

Hi, since I'm not very experienced in surrogate-based optimization I'd like to ask whether there is in pySOT some possibility to use points with already known objective function values before starting optimization itself? Is it possible to do it by own implementation of experimental design? But what are the most important properties which that experimental of design has to fulfil? (I’ve just read some information in online documentation.) Note: My question is motivated by my optimization task. I need to optimize SW (image processing) module where SW efficiency (objective function value) is possible to get by setting parameters and running time-consuming simulation only. But I have already had some parameters settings, which could be used as "initial" point. Is it good idea? Moreover, there are some other known points as a result of previous testing which may be possibly useful as well (?). Radek

dme65 commented 7 years ago

Hi Radek,

This is something we currently don't support. I think this is something we want to support since I can think of several settings where some objective function values are known before the start of the optimization process.

@dbindel : What is the right way of doing this from POAP's point of view?

dme65 commented 7 years ago

This will be added in pySOT 0.1.34. I need to do some more testing before I release the next version, but it's on its way!