Robert Sedgewick
This page always under construction (even though it is an old-school page that needs work).

Robert Sedgewick

Department of Computer Science
Princeton University
Princeton, NJ 08544

Primary professional activities


Recent press


Recent books

Recent talks

Princeton course development

Other professional activities

Past professional appointments

Selected papers

  • "Mellin transforms and asymptotics: finite differences and Rice's integrals" (with P. Flajolet) Theoretical Computer Science A 144, 1995.
  • "The analysis of Heapsort" (with R. Schaffer), J. of Algorithms 13, 1993.
  • "Deterministic skip lists" (with I. Munro and T. Papadakis), Proc. 3rd ACM-SIAM Symp. on Discrete Algorithms, 1992.
  • "Tight lower bounds for Shellsort" (with M. Weiss), J. of Algorithms 11, 1990.
  • "Pairing heaps: a new form of self-adjusting heap," (with M. Fredman, D. Sleator, and R. E. Tarjan) Algorithmica 1, 1, 1986.
  • "Digital search tree analysis revisited" (with P. Flajolet), SIAM J. Computing 15, 2, 1986.
  • "Shortest paths in euclidean graphs" (with J. Vitter), Algorithmica 1, 1, 1986.
  • "A new upper bound for Shellsort," Journal of Algorithms 7, 1986.
  • "Improved Upper Bounds for Shellsort" (with J. Incerpi), J. Computer and System Sciences 31, 2, 1985.
  • "A system for algorithm animation" (with M. Brown) Computer Graphics 18, 3, 1984.
  • Technical reports and old papers
  • Thesis (1975)

    General description of research goals

    Finding efficient algorithms for fundamental practical problems by studying important algorithms at all levels through the design-analysis-implementation cycle. Validating theoretical designs through practical implementations; uncovering fundamental properties of algorithms through careful mathematical performance analyses; comparing algorithms through careful implementation studies.

    Developing general mechanisms relating algorithms, data structures, generating functions and analytic functions such that asymptotic results useful in predicting performance of the algorithms can be derived automatically and economically.

    Investigating the way in which visual representations can provide an understanding of how algorithms gain efficiency, including dynamic graphical simulations of algorithms in operation and high-quality static representations suitable for use in publications.

    Robert Sedgewick