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An efficient algorithm for partial order production
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Annual ACM Symposium on Theory of Computing archive
Proceedings of the 41st annual ACM symposium on Theory of computing table of contents
Bethesda, MD, USA
SESSION: Algorithms and data structures table of contents
Pages 93-100  
Year of Publication: 2009
ISBN:978-1-60558-506-2
Authors
Jean Cardinal  Université Libre de Bruxelles (ULB), Brussels, Belgium
Samuel Fiorini  Université Libre de Bruxelles (ULB), Brussels, Belgium
Gwenaël Joret  Université Libre de Bruxelles (ULB), Brussels, Belgium
Raphaël M. Jungers  Université catholique de Louvain (UCL), Louvain-la-Neuve, Belgium
J. Ian Munro  University of Waterloo, Waterloo, Canada
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

We consider the problem of partial order production: arrange the elements of an unknown totally ordered set T into a target partially ordered set S, by comparing a minimum number of pairs in T. Special cases of this problem include sorting by comparisons, selection, multiple selection, and heap construction.

We give an algorithm performing ITLB + o(ITLB) + O(n) comparisons in the worst case. Here, n denotes the size of the ground sets, and ITLB denotes a natural information-theoretic lower bound on the number of comparisons needed to produce the target poset. The overall complexity of our algorithm is polynomial. This answers a question of Yao (SICOMP, 1989).

Our strategy is to extend the poset S to a weak order W whose corresponding information-theoretic lower bound is provably not much larger than that for S. Taking W instead of S as a target poset, we then solve the problem by applying a multiple selection algorithm that performs not much more than ITLB comparisons.

We base our analysis on the entropy of the target poset S, a quantity that can be efficiently computed and provides a good estimate of ITLB.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
M. Aigner. Producing posets. Discrete Math., 35:1--15, 1981.
 
2
B. Bollobas and P. Hell. Sorting and graphs. In Graphs and order, Banff, Alta., 1984, volume 147 of NATO Adv. Sci. Inst. Ser. C Math. Phys. Sci., pages 169--184, Dordrecht, 1985. Reidel.
 
3
 
4
 
5
6
 
7
J. Chen. Average cost to produce partial orders. In D. Du and X. Zhang, editors, Proc. 5th International Symposium on Algorithms and Computation (ISAAC '94), Beijing, P. R. China, August 25--27, 1994, volume 834 of Lecture Notes in Computer Science, pages 155--163. Springer, 1994.
 
8
V. Chvatal. On certain polytopes associated with graphs. J. Combinatorial Theory Ser. B, 18:138--154, 1975.
 
9
I. Csiszar, J. Korner, L. Lovasz, K. Marton, and G. Simonyi. Entropy splitting for antiblocking corners and perfect graphs. Combinatorica, 10(1):27--40, 1990.
 
10
J.C. Culberson and G.J.E. Rawlins. On the comparison cost of partial orders. Technical Report TR88-01, Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada T6G 2E8, 1988.
11
 
12
M. L. Fredman. How good is the information theory bound in sorting? Theor. Comput. Sci., 1(4):355--361, 1976.
 
13
C. A. R. Hoare. Find (algorithm 65). Commun. ACM, 4(7):321--322, 1961.
 
14
 
15
J. Kahn and M. E. Saks. Balancing poset extensions. Order, 1:113--126, 1984.
 
16
K. Kaligosi, K. Mehlhorn, J. I. Munro, and P. Sanders. Towards optimal multiple selection. In Proc. International Conference on Automata, Languages, and Programming (ICALP'05), Lecture Notes in Computer Science, pages 103--114. Springer--Verlag, 2005.
 
17
J. Korner. Coding of an information source having ambiguous alphabet and the entropy of graphs. In Transactions of the 6th Prague Conference on Information Theory, pages 411--425, 1973.
 
18
N. Linial. The information-theoretic bound is good for merging. SIAM J. Comput., 13(4):795--801, 1984.
 
19
L. Lovasz. Normal hypergraphs and the perfect graph conjecture. Discrete Math., 2(3):253--267, 1972.
 
20
Y. Nesterov and A. Nemirovskii. Interior-point polynomial algorithms in convex programming, volume 13 of SIAM Studies in Applied Mathematics. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1994.
 
21
 
22
 
23
M. E. Saks. The information theoretic bound for problems on ordered sets and graphs. In Graphs and order, Banff, Alta., 1984, volume 147 of NATO Adv. Sci. Inst. Ser. C Math. Phys. Sci., pages 137--168, Dordrecht, 1985. Reidel.
 
24
A. Schonhage. The production of partial orders. In Journees algorithmiques, Ecole Norm. Sup., Paris, 1975, pages 229--246. Asterisque, No. 38--39. Soc. Math. France, Paris, 1976.
 
25
 
26

Collaborative Colleagues:
Jean Cardinal: colleagues
Samuel Fiorini: colleagues
Gwenaël Joret: colleagues
Raphaël M. Jungers: colleagues
J. Ian Munro: colleagues