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-# Adaptive
-
-We propose to use local criteriums for sampling combined with global updates.
-This defines a family of straightforwardly parallelizable algorithms which are useful for intermediary cost simulations.
-
-When your function evaluation is very expensive, full-scale Bayesian sampling will perform better, however, there is a broad class of simulations that are in the right regime for Adaptive to be beneficial.
-
-We can include things like:
-* Asymptotically complexity of algorithms
-* Setting of the problem, which classes of problems can be handled with Adaptive
-* Loss-functions examples (maybe include [Adaptive quantum dots](https://chat.quantumtinkerer.tudelft.nl/chat/channels/adaptive-quantum-dots))
-* Trials, statistics (such as measuring timings)
-* Line simplification algorithm as a general criterium
-* Desirable properties of loss-functions
-* List potential applications