Experiments on union-find algorithms for the disjoint-set data structure

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6049 LNCS, Issue , 2010, Pages 411-423

  Patwary, Md Mostofa Ali ^a   Blair, Jean ^b   Manne, Fredrik ^a  

^a UNIVERSITY OF BERGEN   (Norway)

^b UNITED STATES MILITARY ACADEMY   (United States)

Author keywords

Disjoint set; Experimental algorithms; Union find

Indexed keywords

CLASSICAL ALGORITHMS; DISJOINT SET; EXPERIMENTAL ALGORITHMS; EXPERIMENTAL STUDIES; SIMPLE ALGORITHM; SOFTWARE LIBRARIES; TIME COMPLEXITY; UNION-FIND;

DATA STRUCTURES;

ALGORITHMS;

EID: 78650637936     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13193-6_35     Document Type: Conference Paper

References (20)

1. Bader, D.A., Madduri, K.: GTGraph: A synthetic graph generator suite (2006), http://www.cc.gatech.edu/~kamesh/GTgraph.
2. Banachowski, L.: A complement to Tarjan's result about the lower bound on the complexity of the set union problem. Inf. Proc. Letters 11, 59-65 (1980).
3. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press, Cambridge (2009).
4. Davis, T.A.: University of Florida sparse matrix collection. Submitted to ACM Transactions on Mathematical Software.
5. Dawes, B., Abrahams, D.: The Boost C++ libraries (2009), www.boost.org.
6. Dijkstra, E.W.: A Discipline of Programming. Prentice-Hall, Englewood Cliffs (1976).
7. Galil, Z., Italiano, G.F.: Data structures and algorithms for disjoint set union problems. ACM Comput. Surv. 23(3), 319-344 (1991).
8. Gilbert, J.R., Ng, E.G., Peyton, B.W.: An efficient algorithm to compute row and column counts for sparse Cholesky factorization. SIAM J. Matrix Anal. Appl. 15(4), 1075-1091 (1994).
9. Hynes, R.: A new class of set union algorithms. Master's thesis, Department of Computer Science, University of Toronto, Canada (1998).
10. Liu, J.W.H.: The role of elimination trees in sparse factorization. SIAM J. Matrix Anal. Appl. 11, 134-172 (1990).
11. Manne, F., Patwary, M.M.A.: A scalable parallel union-find algorithm for distributed memory computers. To appear in proc. PPAM 2009. LNCS. Springer, Heidelberg (2009).
12. Melhorn, K., Näher, S.: LEDA, A Platform for Combinatorial Geometric Computing. Cambridge University Press, Cambridge (1999).
13. Osipov, V., Sanders, P., Singler, J.: The filter-Kruskal minimum spanning tree algorithm. In: ALENEX, pp. 52-61 (2009).
14. Patwary, M.M.A., Blair, J., Manne, F.: Efficient Union-Find implementations. Tech. Report 393, Dept. Computer Science, University of Bergen (2010), http://www.uib.no/ii/forskning/reports-in-informatics/reports-in- informatics-2010-2019.
15. Poutré, J.A.L.: Lower bounds for the union-find and the split-find problem on pointer machines. J. Comput. Syst. Sci. 52, 87-99 (1996).
16. Tarjan, R.E.: Efficiency of a good but not linear set union algorithm. J. ACM 22, 215-225 (1975).
17. Tarjan, R.E.: A class of algorithms which require nonlinear time to maintain disjoint sets. J. Comput. Syst. Sci. 18, 110-127 (1979).
18. Tarjan, R.E., van Leeuwen, J.: Worst-case analysis of set union algorithms. J. ACM 31, 245-281 (1984).
19. Wassenberg, J., Bulatov, D., Middelmann, W., Sanders, P.: Determination of maximally stable extremal regions in large images. In: Proc. of Signal Processing, Pattern Recognition, and Applications (SPPRA). Acta Press (2008).
20. Wu, K., Otoo, E., Suzuki, K.: Optimizing two-pass connected-component labeling algorithms. Pattern Anal. Appl. 12, 117-135 (2009).