Peter Hyer. A general technique for implementation of efficient priority queues. Technical Report 33, Odense University, 1994. Also to be published in Proceedings of the Third Israel Symposium on Theory of Computing and Systems.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....and returns the element. Delete(Q; e) Deletes element e from priority queue Q provided that it is known where e is stored in Q (priority queues do not support the searching for an element) The implementation of priority queues is a classical problem in data structures. A few references are [13, 12, 8, 7, 5, 6, 10]. In the amortised sense, 11] the best performance is achieved by binomial heaps [12] They support Delete and DeleteMin in amortised time O(log n) and all other operations in amortised constant time. If we want to perform Insert in worst case constant time two efficient data structures exist. ....

Peter Hyer. A general technique for implementation of efficient priority queues. Technical Report IMADA-94-33, Odense University, 1994.

....of binomial heaps (Carlsson et al. 1988) for example, employs heap ordered pennants. Binomial heaps also form the basis for an implementation of min max priority queues (Khoong Leong, 1993) We have seen that the loser trees in binary binomial heap are always perfectly balanced. H yer (1994) shows that we may relax this condition and use some form of height balancing instead. The first functional implementation of binomial heaps is due to King (1994) King, 1996, p. 28 42) additionally contains a simple proof of correctness. Okasaki (1996b) shows how to turn the amortized Theta(1) ....

Høyer, Peter. 1994 (October). A general technique for implementation of efficient priority queues. Tech. rept. PP-1994-33. Department of Mathematics and Computer Science, Odense University.

....we give in Chapter 5 and 6 are no exception. The most prominent implementations are binomial queues [24, 108] heap ordered (2; 3) trees [1] self adjusting heaps [99] pairing heaps [52] Fibonacci heaps [53] and relaxed heaps [43] Further priority queue implementations can be found in [27, 46, 47, 63, 71, 97, 107]. The best amortized performance achieved by the data structures mentioned above is achieved by binomial queues and Fibonacci heaps. Binomial queues support all operations except Delete, DeleteMin and DecreaseKey in amortized constant time, and Delete, DeleteMin and DecreaseKey in amortized time ....

....of Q and returns the element. Delete(Q; e) Deletes element e from priority queue Q provided that it is known where e is stored in Q (priority queues do not support the searching for an element) The implementation of priority queues is a classical problem in data structures. A few references are [43, 47, 52, 53, 63, 108, 109]. In the amortized sense, 101] the best performance is achieved by binomial heaps [108] They support Delete and DeleteMin in amortized time O(log n) and all other operations in amortized constant time. If we want to perform Insert in worst case constant time a few efficient data This work ....

[Article contains additional citation context not shown here]

Peter Høyer. A general technique for implementation of efficient priority queues. In Proc. 3rd Israel Symposium on Theory of Computing and Systems, pages 57--66, 1995.

.... operations by using efficient priority queues like Fibonacci heaps [12] for maintaining tentative distances, or other priority queue implementations supporting deletion of the minimum key element in amortized or worst case logarithmic time, and decrease key in amortized or worst case constant time [3, 11, 17]. The single source shortest path problem is in NC (by virtue of the all pairs shortest path problem being in NC) and thus a fast parallel algorithm exists, but for general digraphs no work efficient algorithm in NC is known. The best NC algorithm runs in O(log 2 n) time and performs O(n 3 ....

Peter Høyer. A general technique for implementation of efficient priority queues. In Proc. 3rd Israel Symposium on Theory of Computing and Systems, pages 57--66, 1995.

.... operations by using efficient priority queues like Fibonacci heaps [7] for maintaining tentative distances, or other priority queue implementations supporting deletion of the minimum key element in amortized or worst case logarithmic time, and decrease key in amortized or worst case constant time [3, 6, 10]. The single source shortest path problem is in NC (by virtue of the all pairs shortest path problem being in NC) and thus a fast parallel algorithm exists, but for general digraphs no work efficient algorithm in NC is known. The best NC algorithm runs in O(log 2 n) time and performs O(n 3 ....

P. Høyer. A general technique for implementation of efficient priority queues. In Proc. 3rd Israel Symp. on Theory of Comp. and Systems (ISTCS'95), pages 57-- 66, 1995.

.... operations by using efficient priority queues like Fibonacci heaps [13] for maintaining tentative distances, or other priority queue implementations supporting deletion of the minimum key element in amortized or worst case logarithmic time, and decrease key in amortized or worst case constant time [3, 12, 18]. The single source shortest path problem is in NC (by virtue of the all pairs shortest path problem being in NC) and thus a fast parallel algorithm exists, but for general digraphs no work efficient algorithm in NC is known. The best NC algorithm runs in O(log 2 n) time and performs O(n 3 ....

P. Høyer. A general technique for implementation of efficient priority queues. In Proc. 3rd Israel Symposium on Theory of Computing and Systems, pages 57--66, 1995.

....queues are competitive in terms of the constant factors involved in the bounds. Furthermore, they are optimal in the amortized sense, having amortized complexities of O(1) for Insert and Meld, and O(logn) for Delete min [10] Binomial queues have served as the basis for many further extensions [3, 5, 6, 7, 8, 10]. Of these, the most well known is probably the Fibonacci heaps of Fredman and Tarjan [7] which allow decreasing the key of an item at O(1) amortized cost, as well as supporting insertions and melds at O(1) cost (amortized and worst case) Deletion of the minimum item is done at O(logn) amortized ....

....the Fibonacci heaps of Fredman and Tarjan [7] which allow decreasing the key of an item at O(1) amortized cost, as well as supporting insertions and melds at O(1) cost (amortized and worst case) Deletion of the minimum item is done at O(logn) amortized cost. Similar bounds are achieved in [5, 8, 13]. These data structures have theoretical properties superior to binomial queues, but they are also more complex, and may be less advantageous in practice. Several empirical investigations [4, 11, 12] confirm this in the case of Fibonacci heaps. Here, we consider the original binomial queues, and ....

Peter Høyer. A General Technique for Implementation of Efficient Priority Queues. In Proc. 3rd Israel Symposium on Theory of Computing and Systems, Tel Aviv, pages 57--66. IEEE Computer Society Press, 1995.

....of Q and returns the element. Delete(Q; e) Deletes element e from priority queue Q provided that it is known where e is stored in Q (priority queues do not support the searching for an element) The implementation of priority queues is a classical problem in data structures. A few references are [14, 13, 8, 7, 5, 6, 10]. This work was partially supported by the ESPRIT II Basic Research Actions Program of the EC under contract no. 7141 (project ALCOM II) and by the Danish Natural Science Research Council (Grant No. 9400044) Basic Research in Computer Science, Centre of the Danish National Research ....

Peter Høyer. A general technique for implementation of efficient priority queues. Technical Report IMADA-94-33, Odense University, 1994.

....[8] evidence suggests that binomial queues are quite competitive in terms of the constant factors involved in the bounds. Among the data structures for meldable priority queues, binomial queues hold a central position in the sense that they have served as the basis for many further extensions [2, 4, 5, 6, 7, 9]. Of these, the most well known is probably the Fibonacci heaps of Fredman and Tarjan [6] which allow decreasing the key of an element at O(1) amortized cost, as well as supporting insertions and melds at O(1) cost (amortized and worst case) Deletion of the minimum element is done at O(log n) ....

....the Fibonacci heaps of Fredman and Tarjan [6] which allow decreasing the key of an element at O(1) amortized cost, as well as supporting insertions and melds at O(1) cost (amortized and worst case) Deletion of the minimum element is done at O(log n) amortized cost. Similar bounds are achieved in [4, 7]. While these data structures have theoretical properties superior to binomial queues, they are also more complex, and may not be advantageous in practice. Several empirical investigations [3, 11, 12, 13] confirm this in the case of Fibonacci heaps. Here, we consider the original binomial queues, ....

Peter Høyer. A general technique for implementation of efficient priority queues. Technical Report 33, Odense University, 1994. Also to be published in Proceedings of the Third Israel Symposium on Theory of Computing and Systems.

No context found.

Peter Hyer. A general technique for implementation of efficient priority queues. Technical Report 33, Odense University, 1994. Also to be published in Proceedings of the Third Israel Symposium on Theory of Computing and Systems.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC