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A fast algorithm for computing longest common subsequences
Open accessAbstract
Previously published algorithms for finding the longest common subsequence of two sequences of length n have had a best-case running time of O(n 2 ). An algorithm for this problem is presented which has a running time of O((r + n) log n), where r is the total number of ordered pairs of positions at which the two sequences match. Thus in the worst case the algorithm has a running time of O(n 2 log n). However, for those applications where most positions of one sequence match relatively few positions in the other sequence, a running time of O(n log n) can be expected.
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Citations
Cited by
Hall, P. A. V., & Dowling, G. R. (1980) · ACM Computing Surveys · doi:10.1145/356827.356830 · Cited by 586
Roy, C. K., & Cordy, J. R. (2008) · 2008 16th IEEE International Conference on Program Comprehension · doi:10.1109/icpc.2008.41 · Cited by 533
Sim: a utility for detecting similarity in computer programs
Gitchell, D., & Tran, N. (1999) · ACM SIGCSE Bulletin · doi:10.1145/384266.299783 · Cited by 62
Metadata
- Authors
- Hunt, J. W., & Szymanski, T. G.
- Venue
- Communications of the ACM
- Published
- 1977-05
- Volume
- 20
- Issue
- 5
- Pages
- 350-353
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2027506564
- Cited by
- 691
- References
- 0
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: A fast algorithm for computing longest common subsequences. https://app.cochat.ai/papers/10.1145/359581.359603
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- Hunt, J. W., & Szymanski, T. G. (1977). A fast algorithm for computing longest common subsequences. Communications of the ACM. https://doi.org/10.1145/359581.359603
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
