Cycle detection

In computer science, cycle detection or cycle finding is the algorithmic problem of finding a cycle in a sequence of iterated function values. For any function f that maps a finite set S to itself, and any initial value x0 in S, the sequence of iterated function values x 0 , x 1 = f ( x 0 ) , x 2 = f ( x 1 ) , … , x i = f ( x i − 1 ) , … {\displaystyle x_{0},\ x_{1}=f(x_{0}),\ x_{2}=f(x_{1}),\ \dots ,\ x_{i}=f(x_{i-1}),\ \dots } must eventually use the same value twice: there must be some pair of distinct indices i and j such that xi = xj. Once this happens, the sequence must continue periodically, by repeating the same sequence of values from xi to xj − 1. Cycle detection is the problem of finding i and j, given f and x0. Several algorithms are known for finding cycles quickly and with little memory. Robert W. Floyd's tortoise and hare algorithm moves two pointers at different speeds through the sequence of values until they both point to equal values. Alternatively, Brent's algorithm is based on the idea of exponential search. Both Floyd's and Brent's algorithms use only a constant number of memory cells, and take a number of function evaluations that is proportional to the distance from the start of the sequence to the first repetition. Several other algorithms trade off larger amounts of memory for fewer function evaluations. The applications of cycle detection include testing the quality of pseudorandom number generators and cryptographic hash functions, computational number theory algorithms, detection of infinite loops in computer programs and periodic configurations in cellular automata, automated shape analysis of linked list data structures, and detection of deadlocks for transactions management in DBMS.