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17
Splay trees, DavenportSchinzel sequences, and the deque conjecture
, 2007
"... We introduce a new technique to bound the asymptotic performance of splay trees. The basic idea is to transcribe, in an indirect fashion, the rotations performed by the splay tree as a DavenportSchinzel sequence S, none of whose subsequences are isomorphic to fixed forbidden subsequence. We direct ..."
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Cited by 15 (5 self)
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We introduce a new technique to bound the asymptotic performance of splay trees. The basic idea is to transcribe, in an indirect fashion, the rotations performed by the splay tree as a DavenportSchinzel sequence S, none of whose subsequences are isomorphic to fixed forbidden subsequence. We direct this technique towards Tarjan’s deque conjecture and prove that n deque operations require O(nα ∗ (n)) time, where α ∗ (n) is the minimum number of applications of the inverseAckermann function mapping n to a constant. We are optimistic that this approach could be directed towards other open conjectures on splay trees such as the traversal and split conjectures.
Experimental Evaluation of a New Shortest Path Algorithm (Extended Abstract)
, 2002
"... We evaluate the practical eciency of a new shortest path algorithm for undirected graphs which was developed by the rst two authors. This algorithm works on the fundamental comparisonaddition model. Theoretically, this new algorithm outperforms Dijkstra's algorithm on sparse graphs for t ..."
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Cited by 12 (4 self)
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We evaluate the practical eciency of a new shortest path algorithm for undirected graphs which was developed by the rst two authors. This algorithm works on the fundamental comparisonaddition model. Theoretically, this new algorithm outperforms Dijkstra's algorithm on sparse graphs for the allpairs shortest path problem, and more generally, for the problem of computing singlesource shortest paths from !(1) different sources. Our extensive experimental analysis demonstrates that this is also the case in practice. We present results which show the new algorithm to run faster than Dijkstra's on a variety of sparse graphs when the number of vertices ranges from a few thousand to a few million, and when computing singlesource shortest paths from as few as three different sources.
Proximate point searching
 In Proceedings of the 14th Canadian Conference on Computational Geometry (CCCG
, 2002
"... In the 2D point searching problem, the goal is to preprocess n points P = {p1,..., pn} in the plane so that, for an online sequence of query points q1,..., qm, it can quickly determined which (if any) of the elements of P are equal to each query point qi. This problem can be solved in O(log n) time ..."
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Cited by 11 (5 self)
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In the 2D point searching problem, the goal is to preprocess n points P = {p1,..., pn} in the plane so that, for an online sequence of query points q1,..., qm, it can quickly determined which (if any) of the elements of P are equal to each query point qi. This problem can be solved in O(log n) time by mapping the problem to one dimension. We present a data structure that is optimized for answering queries quickly when they are geometrically close to the previous successful query. Specifically, our data structure executes queries in time O(log d(qi−1, qi)), where d is some distance function between two points, and uses O(n log n) space. Our structure works with a variety of distance functions. In contrast, it is proved that, for some of the most intuitive distance functions d, it is impossible to obtain an O(log d(qi−1, qi)) runtime, or any bound that is o(log n).
A framework for speeding up priorityqueue operations
, 2004
"... Abstract. We introduce a framework for reducing the number of element comparisons performed in priorityqueue operations. In particular, we give a priority queue which guarantees the worstcase cost of O(1) per minimum finding and insertion, and the worstcase cost of O(log n) with at most log n + O ..."
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Cited by 8 (8 self)
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Abstract. We introduce a framework for reducing the number of element comparisons performed in priorityqueue operations. In particular, we give a priority queue which guarantees the worstcase cost of O(1) per minimum finding and insertion, and the worstcase cost of O(log n) with at most log n + O(1) element comparisons per minimum deletion and deletion, improving the bound of 2log n + O(1) on the number of element comparisons known for binomial queues. Here, n denotes the number of elements stored in the data structure prior to the operation in question, and log n equals max {1,log 2 n}. We also give a priority queue that provides, in addition to the abovementioned methods, the prioritydecrease (or decreasekey) method. This priority queue achieves the worstcase cost of O(1) per minimum finding, insertion, and priority decrease; and the worstcase cost of O(log n) with at most log n + O(log log n) element comparisons per minimum deletion and deletion. CR Classification. E.1 [Data Structures]: Lists, stacks, and queues; E.2 [Data
Key independent optimality
 In International Symp. on Algorithms and Computation
, 2002
"... A new form of optimality for comparison based static dictionaries is introduced. This type of optimality, keyindependent optimality, is motivated by applications that assign key values randomly. It is shown that any data structure that is keyindependently optimal is expected to execute any access s ..."
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Cited by 8 (2 self)
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A new form of optimality for comparison based static dictionaries is introduced. This type of optimality, keyindependent optimality, is motivated by applications that assign key values randomly. It is shown that any data structure that is keyindependently optimal is expected to execute any access sequence where the key values are assigned arbitrarily to unordered data as fast as any offline binary search tree algorithm, within a multiplicative constant. Asymptotically tight upper and lower bounds are presented for keyindependent optimality. Splay trees are shown to be keyindependently optimal. 1
Pairing heaps with O(log log n) decrease cost
 In 20th ACMSIAM Symposium on Discrete Algorithms
, 2009
"... We give a variation of the pairing heaps for which the time bounds for all the operations match the lower bound proved by Fredman for a family of similar selfadjusting heaps. Namely, our heap structure requires O(1) for insert and findmin, O(log n) for deletemin, and O(log log n) for decreasekey a ..."
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Cited by 5 (2 self)
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We give a variation of the pairing heaps for which the time bounds for all the operations match the lower bound proved by Fredman for a family of similar selfadjusting heaps. Namely, our heap structure requires O(1) for insert and findmin, O(log n) for deletemin, and O(log log n) for decreasekey and meld (all the bounds are in the amortized sense except for findmin). 1
MultiSplay Trees
, 2006
"... In this thesis, we introduce a new binary search tree data structure called multisplay tree and prove that multisplay trees have most of the useful properties different binary search trees (BSTs) have. First, we demonstrate a close variant of the splay tree access lemma [ST85] for multisplay tree ..."
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Cited by 3 (0 self)
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In this thesis, we introduce a new binary search tree data structure called multisplay tree and prove that multisplay trees have most of the useful properties different binary search trees (BSTs) have. First, we demonstrate a close variant of the splay tree access lemma [ST85] for multisplay trees, a lemma that implies multisplay trees have the O(log n) runtime property, the static finger property, and the static optimality property. Then, we extend the access lemma by showing the remassing lemma, which is similar to the reweighting lemma for splay trees [Geo04]. The remassing lemma shows that multisplay trees satisfy the working set property and keyindependent optimality, and multisplay trees are competitive to parametrically balanced trees, as defined in [Geo04]. Furthermore, we also prove that multisplay trees achieve the O(log log n)competitiveness and that sequential access in multisplay trees costs O(n). Then we naturally extend the static model to allow insertions and deletions and show how to carry out these operations in multisplay trees to achieve
Parameterized Analysis of Paging and List Update Algorithms
"... It is wellestablished that input sequences for paging and list update have locality of reference. In this paper we analyze the performance of algorithms for these problems in terms of the amount of locality in the input sequence. We define a measure for locality that is based on Denning’s working ..."
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Cited by 2 (0 self)
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It is wellestablished that input sequences for paging and list update have locality of reference. In this paper we analyze the performance of algorithms for these problems in terms of the amount of locality in the input sequence. We define a measure for locality that is based on Denning’s working set model and express the performance of well known algorithms in term of this parameter. This introduces parameterizedstyle analysis to online algorithms. The idea is that rather than normalizing the performance of an online algorithm by an (optimal) offline algorithm, we explicitly express the behavior of the algorithm in terms of two more natural parameters: the size of the cache and Denning’s working set measure. This technique creates a performance hierarchy of paging algorithms which better reflects their intuitive relative strengths. It also reflects the intuition that a larger cache leads to a better performance. We obtain similar separation for list update algorithms. Lastly, we show that, surprisingly, certain randomized algorithms which are superior to MTF in the classical model are not so in the parameterized case, which matches experimental results.
Queaps
, 2002
"... We present a new priority queue data structure, the queap, that executes insertion in O(1) amortized time and extractmin in O(log(k + 2)) amortized time if there are k items that have been in the heap longer than the item to be extracted. Thus if the operations on the queap are rstin rstout ..."
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Cited by 1 (0 self)
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We present a new priority queue data structure, the queap, that executes insertion in O(1) amortized time and extractmin in O(log(k + 2)) amortized time if there are k items that have been in the heap longer than the item to be extracted. Thus if the operations on the queap are rstin rstout, as on a queue, each operation will execute in constant time. This idea of trying to make operations on the least recently accessed items fast, which we call the queueish property, is a natural complement to the working set property of certain data structures, such as splay trees and pairing heaps, where operations on the most recently accessed data execute quickly. However, we show that the queueish property is in some sense more dicult than the working set property by demonstrating that it is impossible to create a queueish binary search tree, but that many search data structures can be made almost queueish with a O(log log n) amortized extra cost per operation.