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Experimental Algorithmics (JEA)

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JEA: Resources for Experimentors

 

Articles on Experimentation Methodology

  • “Presenting data from experiments in algorithmics,” by Peter Sanders, published in Experimental algorithmics: from algorithm design to robust and efficient software, Springer-Verlag, 2002.

  • Several position papers on methodological issues appear in Data Structures, Near Neighbor Searches, and Methodology: Fifth and Sixth DIMACS Implementation Challenges, M. H. Goldwasser, D. S. Johnson, and C. C. McGeoch, eds.  DIMACS Monographs Vol 59, MAA Press, 2002.
  • "A Theoretician's Guide to the Experimental Analysis of Algorithms," by David S. Johnson, appeared in Fifth and Sixth DIMACS Implentation Challenges (see above). Here is the Postscript version. Here is the PDF version.
  • "A Bibliography of Algorithm Experimentation," by C. C. McGeoch appeared in Fifth and Sixth DIMACS Implentation Challenges (see above).
  • Bridging the Gap Between Theory and Practice, an essay on JEA by Bernard Moret, published in the Journal of Electronic Publishing
  • "How to present a paper in experimental algorithmics," by Catherine C. McGeoch and Bernard Moret, published in SIGACT News, Vol. 30, No. 4, December 1999.
  • "Toward an Experimental Method in Algorithm Analysis," by Catherine C. McGeoch, INFORMS Journal on Computing, Vol. 8 No. 1, Winter 1995.b
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Software and Algorithm Collections

  • The Stony Brook Algorithm Repository. Well-organized meta-site contains links to implementations of algorithms for combinatorial problems, with ratings and commentary.
  • The DIMACS Implementation Challenge. Code and test sets contributed by participants in the DIMACS Challenges. A widely used input format for networks and graphs is described at this site.
  • Algorithmic Solutions (formerly known as LEDA). Efficient C++ implementations of standard combinatorial algorithms and data structures. You must purchase a license to use these products.
  • Mike Trick's OR Page. Resources of all kinds for problems of interest to the Operations Research community.
  • Evaluation of Intelligent Systems. Statistical and data analysis resources and adivce for conducting experiments on algorithms for problems in intelligent systems.
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