Establishes Ω(n/ε²) query lower bounds for approximating correlation clustering cost and partitions under memory constraints in adjacency-matrix and general graph models.
Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) , pages =
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Query Lower Bounds for Correlation Clustering under Memory Constraints
Establishes Ω(n/ε²) query lower bounds for approximating correlation clustering cost and partitions under memory constraints in adjacency-matrix and general graph models.