A new flexible algorithm for the longest common subsequence problem

  • Claus Rick
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 937)


A new algorithm that is efficient for both short and long longest common subsequences is presented. It also improves on previous algorithms for longest common subsequences of intermediate length. Thus, it is more flexible and can be used for a wider range of applications than others. The algorithm is based on the well-known paradigm of computing dominant matches and was obtained through a kind of dualization. Some experimental results are given, too.


Previous Algorithm Dynamic Programming Approach Longe Common Subsequence Alphabet Size Longe Common Subsequence 
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  1. 1.
    A. Apostolico, C. Guerra: The Longest Common Subsequence Problem Revisited, Algorithmica, Vol. 2, 1987, 315–336.MathSciNetGoogle Scholar
  2. 2.
    F. Y. L. Chin, C. K. Poon: A Fast Algorithm for Computing Longest Common Subsequences of Small Alphabet Size, Journal of Information Processing, Vol. 13, No. 4, 1990, 463–469.Google Scholar
  3. 3.
    F. Y. L. Chin, C. K. Poon: Performance Analysis of Some Simple Heuristics for Computing Longest Common Subsequences, Algorithmica, Vol. 12, 1994, 293–311.CrossRefGoogle Scholar
  4. 4.
    V. Dančík: Expected Length of Longest Common Subsequences, PhD thesis, University of Warwick, 1994.Google Scholar
  5. 5.
    D. Eppstein, Z. Galil, R. Giancarlo, G. F. Italiano: Sparse Dynamic Programming I: Linear Cost Functions, Journal of the ACM, Vol. 39, No. 3, 1992, 519–545.CrossRefGoogle Scholar
  6. 6.
    D. S. Hirschberg: Algorithms for the Longest Common Subsequence Problem, Journal ACM, Vol. 24, Oct. 1977, 664–675.Google Scholar
  7. 7.
    W. J. Hsu, M. W. Du: New Algorithms for the LCS Problem, Journal of Computer and System Sciences 29, 1984, 133–152.Google Scholar
  8. 8.
    J. W. Hunt, T. G. Szymanski: A Fast Algorithm for Computing Longest Common Subsequences, Comm. ACM, Vol. 20, May 1977, 350–353.CrossRefGoogle Scholar
  9. 9.
    W. J. Masek, M. S. Paterson: A Faster Algorithm Computing String Edit Distances, Journal of Computer and System Sciences 20, 1980, 18–31.Google Scholar
  10. 10.
    E. W. Myers: An O(N D) Difference Algorithm and Its Variations, Algorithmica, Vol. 1, 1986, 251–266.Google Scholar
  11. 11.
    N. Nakatsu, Y. Kambayashi, S. Yajima: A Longest Common Subsequence Algorithm Suitable for Similar Text Strings, Acta Informatica 18, 1982, 171–179.Google Scholar
  12. 12.
    M. Paterson, V. Dancik: Longest Common Subsequences, Proceedings of the 19th Intern. Symp. on Mathematical Foundations of Computer Science, Vol. 841 of LNCS, 1994, 127–142.Google Scholar
  13. 13.
    C. Rick: New Algorithms for the Longest Common Subsequence Problem, Research Report No. 85123-CS, University of Bonn, 1994.Google Scholar
  14. 14.
    D. Sankoff, J. B. Kruskal (Ed.): Time Warps, String Edits and Macromolecules: The Theory and Practice of Sequence Comparison, Addison-Wesley: Reading, MA, 1983.Google Scholar
  15. 15.
    E. Ukkonen: Algorithms for Approximate String Matching, Information and Control 64, 1985, 100–118.Google Scholar
  16. 16.
    R. A. Wagner, M. J. Fischer: The String to String Correction Problem, Journal ACM, Vol. 21, 1974, 168–173.Google Scholar
  17. 17.
    S. Wu, U. Manber, G. Myers, W. Miller: An O(NP) Sequence Comparison Algorithm, Information Processing Letters 35, 1990, 317–323.MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Claus Rick
    • 1
  1. 1.Computer Science Department IVUniversity of BonnBonnGermany

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