Extensions of Self-Improving Sorters
Siu-Wing Cheng, Kai Jin, Lie Yan · Algorithmica · 2019
Ailon et al. (SIAM J Comput 40(2):350–375, 2011 ) proposed a self-improving sorter that tunes its performance to an unknown input distribution in a training phase. The input numbers \(x_1,x_2,\ldots ,x_n\) come from a product distribution, that is, each \(x_i\) is drawn independently from an arbitrary distribution \({{{\mathcal {D}}}}_i\) . We study two relaxations of this requirement. The first extension models hidden classes in the input. We consider the case that numbers in the same class are governed by linear functions of the same hidden random parameter. The second extension considers a hidden mixture of product distributions.