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I am trying to build some kind of decision tree that could be used to decide on the optimization method applicable for given problem based on assumptions about objectvie function, type of constraints, search space, complexity etc.

What I am looking for is a taxonomy of optimization methods (as exhaustive as possible) that I could possibly use to build such a tree.

So far the best graph I found was this: (source) enter image description here

which I think is good enough, but I was hoping to maybe get some other perspective - ideally from the literature. I looked at Convex Optimization, Stephen Boyd and Numerical Optimization, Jorge Nocedal Stephen J. Wright but did not find an explicit and concise taxonomy like the one in the graph above. Numerical Optimization mentions some most important dichotomies (e.g. continuous vs. discrete, constraint vs. unconstrained, global vs. local, stochastic vs. deterministic, convex vs. non-convex) in the introduction chapter but without any further sub-divisions.

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  • $\begingroup$ The classification is good, but it only explore problem based optimization, not algorithm based. In algorithm based, there will be genetic algorithm and neural network approach. This is some of example of such classification 1, 2 $\endgroup$ Oct 15, 2021 at 22:55

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An updated version of the taxonomy on NEOS is here: https://neos-guide.org/guide/types/

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