Convergence Rates in the Probabilistic Analysis of Algorithms

Ralph Neininger, Jasmin Straub, Clemens Heuberger, Michael Drmota
2020 International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms  
In this extended abstract a general framework is developed to bound rates of convergence for sequences of random variables as they mainly arise in the analysis of random trees and divide-and-conquer algorithms. The rates of convergence are bounded in the Zolotarev distances. Concrete examples from the analysis of algorithms and data structures are discussed as well as a few examples from other areas. They lead to convergence rates of polynomial and logarithmic order. Our results show how to
more » ... in a significantly better bound for the rate of convergence when the limiting distribution is Gaussian.
doi:10.4230/lipics.aofa.2020.22 dblp:conf/aofa/NeiningerS20 fatcat:ellliex4czd77gtdfe6fcpw3iy