In this topic we will learn how to compare the performance of algorithms on large inputs using asymptotic notations — the Big Oh and the Big Omega. In our examples, we will mainly deal with running times of algorithms (so-called time complexity); analogously one can analyze memory usage (so-called space complexity), too. We will also investigate how the success probability of an algorithm can depend on input size or security parameters using the concepts negligible, overwhelming and noticeable.

asymptotics.txt · Last modified: 2014/11/14 03:21 by aleksei