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This paper considers correlation clustering on unweighted complete graphs. We give a combinatorial algorithm that returns a single clustering solution that is simultaneously O(1)-approximate for all 𝓁_p-norms of the disagreement vector; in other words, a combinatorial O(1)-approximation of the all-norms objective for correlation clustering. This is the first proof that minimal sacrifice is needed in order to optimize different norms of the disagreement vector. In addition, our algorithm is the first combinatorial approximation algorithm for the 𝓁₂-norm objective, and more generally the first combinatorial algorithm for the 𝓁_p-norm objective when 1 < p < ∞. It is also faster than all previous algorithms that minimize the 𝓁_p-norm of the disagreement vector, with run-time O(n^ω), where O(n^ω) is the time for matrix multiplication on n × n matrices. When the maximum positive degree in the graph is at most Δ, this can be improved to a run-time of O(nΔ² log n). 

We consider the problem in which n points arrive online over time, and upon arrival must be irrevocably assigned to one of k clusters where the objective is the standard k-median objective. Lower-bound instances show that for this problem no online algorithm can achieve a competitive ratio bounded by any function of n. Thus we turn to a beyond worst-case analysis approach, namely we assume that the online algorithm is a priori provided with a predicted budget B that is an upper bound to the optimal objective value (e.g., obtained from past instances). Our main result is an online algorithm whose competitive ratio (measured against B) is solely a function of k. We also give a lower bound showing that the competitive ratio of every algorithm must depend on k.