The work proves that approximating correlation clustering to additive εn² error requires Ω(n/ε²) adjacency-matrix queries, with stronger bounds under memory constraints in random and general query 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
The work proves that approximating correlation clustering to additive εn² error requires Ω(n/ε²) adjacency-matrix queries, with stronger bounds under memory constraints in random and general query models.