What is the difference between surrogateopt and bayeopt?

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I would like to ask the general difference between these 2 algorithms in MATLAB. I understand that Bayesian Optimization uses a Gaussian Process modeling that uses probability theory. In Bayesian Optimization, to my understanding you also use radial basis function to build your gaussian process model (which is sometimes called a surrogate model?), while SurrogateOpt also does that?
It would be very helpful if I can understand the nuances and differences between the two, at a theoretical/MATLAB implementation.
Thank you!

Answers (1)

Alan Weiss
Alan Weiss on 8 Mar 2023
The algorithms have fairly complete descriptions:
They are similar in some ways, but differ in a host of details. bayesopt assumes that evaluating an objective function gives a random answer. surrogateopt, like all other optimization algorithms, assumes that evaluating an objective function gives a deterministic answer. The algorithms have so many differences that it is not worthwhile listing them; please, just look at the links I gave. Note that the algorithms behave somewhat differently under parallel computation than serial computation.
Alan Weiss
MATLAB mathematical toolbox documentation




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