On linear-time deterministic algorithms for optimization problems in fixed dimension

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Coresets via Bilevel Optimization for Continual Learning and Streaming (2020, methodology): “Relying on this idea, we use a variant of the merge-reduce framework of [10].”
Practical Coreset Constructions for Machine Learning (2017, methodology): “The composition properties of strong coresets can again be leveraged by applying the merge-reduce framework [Chazelle and Matoušek, 1996]. The idea is to construct and store in memory a coreset for every block of consecutive points arriving in a stream. As soon as we have two coresets in memory we m”