I am having a hard time trying to make sense of the phrase "Scalable optimization". I read from a few papers discussing that some global optimization solvers like BARON, ANTIGONE, etc. do not scale well with the number of scenarios.

I quote from "Languages and Tools for Optimization of Large−Scale Systems":

Model scalability means the granularity of the model behaviour should be easy to change, without the need to re-build the model.

My very basic understanding and experience with optimization is that optimization would not work well if scaling of the problem is not done properly, and computational time increases with problem size or dimension (large scale system). As to model scalability, I thought a model is built for a specific subject/process and its granularity/level of detail cannot be changed unless we rebuild it?

Can someone shed some light on this? 🙏🏻


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