A Renee Equation for Algorithmic Complexity

  • Keye Martin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2215)


We introduce a notion of complexity for the renee equation and use it to develop a method for analyzing search algorithms which enables a uniform treatment of techniques that manipulate discrete data, like linear and binary search of lists, as well as those which manipulate continuous data, like methods for zero finding in numerical analysis.


Search Method Binary Search Algorithmic Complexity Recursive Function Domain Theory 
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  1. 1.
    S. Abramsky and A. Jung. Domain Theory. In S. Abramsky, D. M. Gabbay, T. S. E. Maibaum, editors, Handbook of Logic in Computer Science, vol. III. Oxford University Press, 1994Google Scholar
  2. 2.
    Neil Jones. Computability and complexity: from a programming perspective. MIT Press, 1997.Google Scholar
  3. 3.
    K. Martin. A foundation for computation. Ph.D. thesis, Department of Mathematics, Tulane University, 2000.Google Scholar
  4. 4.
    K. Martin. A principle of induction. Proceedings of the European Association for Computer Science Logic, Lecture Notes in Computer Science, Springer-Verlag, 2001, to appear.Google Scholar
  5. 5.
    K. Martin. The measurement process in domain theory. Proceedings of the 27th International Colloquium on Automata, Languages and Programming (ICALP), Lecture Notes in Computer Science, vol. 1853, Springer-Verlag, 2000.Google Scholar
  6. 6.
    K. Martin. Unique fixed points in domain theory. Proceedings of MFPS XVII, Electronic Notes in Theoretical Computer Science, vol. 45, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Keye Martin
    • 1
  1. 1.Oxford University Computing LaboratoryOxford

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