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A Generalization of Self-Improving Algorithms
2020
International Symposium on Computational Geometry
Ailon et al. [SICOMP'11] proposed self-improving algorithms for sorting and Delaunay triangulation (DT) when the input instances x₁,⋯,x_n follow some unknown product distribution. That is, x_i comes from a fixed unknown distribution 𝒟_i, and the x_i's are drawn independently. After spending O(n^{1+ε}) time in a learning phase, the subsequent expected running time is O((n+ H)/ε), where H ∈ {H_S,H_DT}, and H_S and H_DT are the entropies of the distributions of the sorting and DT output,
doi:10.4230/lipics.socg.2020.29
dblp:conf/compgeom/ChengCJW20
fatcat:vvsv2rve6zfrbpq4fdwahb2asa