Cycle Detection: Difference between revisions

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[[File:Cycle Detection - Time.png|1000px]]
[[File:Cycle Detection - Time.png|1000px]]
== Space Complexity Graph ==
[[File:Cycle Detection - Space.png|1000px]]
== Time-Space Tradeoff ==
[[File:Cycle Detection - Pareto Frontier.png|1000px]]


== References/Citation ==  
== References/Citation ==  


https://www-sciencedirect-com.ezproxy.canberra.edu.au/science/article/pii/0304397585900441?via%3Dihub
https://www-sciencedirect-com.ezproxy.canberra.edu.au/science/article/pii/0304397585900441?via%3Dihub

Latest revision as of 09:07, 28 April 2023

Description

Cycle detection or cycle finding is the algorithmic problem of finding a cycle in a sequence of iterated function values.

Parameters

$t_f$: time to perform one evaluation of $f$

$\mu$: the starting index of the cycle

$\lambda$: the period of the cycle

$M$: number of values stored

Table of Algorithms

Name Year Time Space Approximation Factor Model Reference
Sedgewick; Szymanski; and Yao 1982 $(\mu + \lambda)({1}+\Theta({1}/sqrt(M)))$ M Exact Deterministic Time & Space
Nivasch 2004 $O(\mu + \lambda)$ $O(\log\mu)$ Exact Deterministic Time & Space
Floyd's tortoise and hare algorithm 1967 $O((\lambda + \mu)$ t_f) $O({1})$ Exact Deterministic Time
Brent's algorithm 1973 $O((\lambda + \mu)$ t_f) $O({1})$ Exact Deterministic Time
Gosper's algorithm 1978 $O((\lambda + \mu)$ log(\lambda + \mu) t_f) \Theta(log(\mu + \lambda)) Exact Deterministic Time & Space

Time Complexity Graph

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References/Citation

https://www-sciencedirect-com.ezproxy.canberra.edu.au/science/article/pii/0304397585900441?via%3Dihub