The String-to-String Correction Problem
Journal of the ACM (JACM)
A fast algorithm for computing longest common subsequences
Communications of the ACM
A linear space algorithm for computing maximal common subsequences
Communications of the ACM
An efficient and accurate method for evaluating time series similarity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Bit-Parallel Algorithm for the Constrained Longest Common Subsequence Problem
Fundamenta Informaticae
Solving longest common subsequence and related problems on graphical processing units
Software—Practice & Experience
A fast longest common subsequence algorithm for similar strings
LATA'10 Proceedings of the 4th international conference on Language and Automata Theory and Applications
Utilizing dynamically updated estimates in solving the longest common subsequence problem
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
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Let A and B be strings of common length n. Define LLCS(A, B) to be the length of the longest common subsequence of A and B. Hunt and Szymanski presented an algorithm for finding LLCS(A, B) with time complexity O((r + n)logn), where r is the number of elements in the set {(i, j)|A[i] = B[j]}. In the worst case the algorithm has running time of O(n2logn). We present an improvement to this algorithm which changes the time complexity to O(r + n(LLCS(A, B) + logn)). Some experimental results show dramatic improvements for large n.