S. R. Buss and P. N. Yianilos. Linear and o(n log n) time minimumcost matching algorithms for quasi-convex tours. SIAM J. of Computing, 27(1):170--201, 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....M . Exploiting the fact that the weights are Euclidean distances, Vaidya obtained an O(n 2:5 log n) time algorithm for computing M [67] A number of efficient algorithms have been developed for computing a minimum weight bipartite Euclidean matching when P and Q have some special structure [10, 17, 48], but no progress has been made when P and Q are arbitrary sets of points in the plane. Recently, Alon and Itai [33] have proposed an O(n 1:5 ) time algorithm for computing a bottleneck Euclidean bipartite matching, in which one wishes to minimize the maximum weight of an edge in the ....

S. Buss and P. Yianilos, Linear and O(n log n) time minimum-cost matching algorithms for quasi-convex tours, Proc. 5th Annual ACM-SIAM Symp. Discrete Algorithms, 1994, 65--76.

....M . Exploiting the fact that the weights are Euclidean distances, Vaidya obtained an O(n 2:5 log n) time algorithm for computing M [67] A number of efficient algorithms have been developed for computing a minimum weight bipartite Euclidean matching when P and Q have some special structure [10, 17, 48], but no progress has been made when P and Q are arbitrary sets of points in the plane. Recently, Alon and Itai [33] have proposed an O(n 1:5 ) time algorithm for computing a bottleneck Euclidean bipartite matching, in which one wishes to minimize the maximum weight of an edge in the ....

S. Buss and P. Yianilos, Linear and O(n log n) time minimum-cost matching algorithms for quasi-convex tours, Proc. 5th Annual ACM-SIAM Symp. Discrete Algorithms, 1994, 65--76.

....n) time algorithm for the Euclidean distance, and an O(n 2 log 3 n) time algorithm for the L1 distance. The solution of the Euclidean case has recently been improved by Agarwal, Efrat and Sharir [2] to O(n 2 ) 1 . However, the resulting algorithms remain relatively complicated. See also [11] for fast algorithms for other types of graphs related to geometric configurations. For computing Match(A; B) we use a parametric search technique, reminiscent of the one proposed by Megiddo [45] We introduce in Section 3 an oracle that determines, for a parameter r, whether Match(A; B) r. In ....

S. Buss and P. Yianilos, Linear and O(n log n) time minimum-cost matching algorithms for quasi-convex tours, Proceedings 5 Annual ACM-SIAM Symposium on Discrete Algorithms, 1994, 65--76.

....property is weaker than the (inverse) Monge property and by the observation made above also weaker than the Kalmanson property. In Section 3 we already mentioned that the minimum cost matching problem becomes easier if the underlying weights satisfy the (inverse) Monge condition. Buss and Yianilos [41] show that this remains true for the weaker quasi convex property. In this way, they generalize or improve upon results of Marcotte and Suri [94] Karp and Li [84] and Aggarwal, Klawe, Moran, Shor and Wilber [8] Specifically, Buss and Yianilos obtain an O(n log n) time algorithm for the ....

S.R. Buss and P.N. Yianilos, Linear and O(n log n) time minimum-cost matching algorithms for quasiconvex tours, in: Proceedings of the Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, 1994, pp. 65--76.

....algorithm for the Euclidean distance, and an O(n 2 log 3 n) time algorithm for the L1 distance. The solution of the Euclidean case has recently been improved by Agarwal et al. 2] to O(n 2 ) 1 . However, the resulting algorithms remain relatively complicated. See also Buss and Yianilos [11] for fast algorithms for other types of graphs related to geometric configurations. For computing Match(A; B) we introduce in Section 3 an oracle that determines, for a parameter r, whether Match(A; B) r. The exact running times depend on the norm and the dimension. The oracle is then used to ....

S. Buss and P. Yianilos, Linear and O(n log n) time minimum-cost matching algorithms for quasi-convex tours, Proceedings 5 Annual ACM-SIAM Symposium on Discrete Algorithms, 1994, 65--76.

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S. R. Buss and P. N. Yianilos. Linear and o(n log n) time minimumcost matching algorithms for quasi-convex tours. SIAM J. of Computing, 27(1):170--201, 1998.

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S. R. Buss and P. N. Yianilos, Linear and O(n log n) time minimum-cost matching algorithms for quasi-convex tours. To appear in Siam J. Comput.. An extended abstract of this paper appeared in Proceedings of the 5th Annual ACM-SIAM Symposium on Discrete Algorithms, 1994, pp. 65-76., 199?

....for bigrams. The result was that small database could be searched on early personal computers without using the PF474 chip. The computational heart of Friendly Finder was also made available under license, and named P2. 4. A transition to the bipartite matching viewpoint took place with [1, 2], the algorithms being improved and in some cases simplified. The result is entirely new algorithms that are still of the same family. The LikeIt facility is the first implementation based on these new developments. The algorithms of [1] lead to linear time algorithms for a large class of graph ....

....to the bipartite matching viewpoint took place with [1, 2] the algorithms being improved and in some cases simplified. The result is entirely new algorithms that are still of the same family. The LikeIt facility is the first implementation based on these new developments. The algorithms of [1] lead to linear time algorithms for a large class of graph cost functions, including the simple linear costs used by LikeIt. Linear time matching algorithms for this particularly simple special case were first presented in [6] The LikeIt approach, used in effect by Friendly Finder [10] is to ....

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S. R. Buss and P. N. Yianilos, Linear and o(n log n) time minimumcost matching algorithms for quasi-convex tours, in Proceedings of the 5th Annual ACM-SIAM Symposium on Discrete Algorithms, 1994, pp. 65--76. To appear SIAM Journal on Computing.

....University of California, San Diego, La Jolla, CA 92093 0112. Email: sbuss ucsd.edu. y NEC Research Institute, 4 Independence Way, Princeton, NJ 08540 and Department of Computer Science, Princeton University, Princeton, NJ 08544. Email: pny research.nj.nec.com. z Earlier related works [4, 3] of the authors may be obtained by anonymous ftp from euclid.ucsd.edu, directory pub sbuss research, filenames quasiconvex.ps and quasiconvex.Ccode.ps. 1 Introduction Search and the implied process of comparison, are fundamental notions of both theoretical and applied computer science. The ....

....near the beginning of ff , etc. These enhancements can be very useful in practice to improve the perceived quality of the optimal assignment; however, they make little difference to the algorithms described in this paper, so we shall use just the simple notion of cost as defined above. In [4] the authors have developed linear time and near linear time algorithms for finding minimum cost bipartite matchings when the cost function is concave down. Although space does not permit us to review the details of this algorithm, we shall review a few keys aspects of the algorithm which are ....

[Article contains additional citation context not shown here]

S. R. Buss and P. N. Yianilos, Linear and O(n log n) time minimum-cost matching algorithms for quasi-convex tours. To appear in Siam J. Comput.. An extended abstract of this paper appeared in Proceedings of the 5th Annual ACM-SIAM Symposium on Discrete Algorithms, 1994, pp. 65-76., 199?

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Samuel R. Buss and Peter N. Yianilos. Linear and O(n log n) Time Minimum-Cost Matching Algorithms for Quasi-Convex Tours. SIAM Journal on Computing, 27(1):170--201, 1998.

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