Results 1 
5 of
5
Data Structural Bootstrapping, Linear Path Compression, and Catenable Heap Ordered Double Ended Queues
 SIAM Journal on Computing
, 1992
"... A deque with heap order is a linear list of elements with realvalued keys which allows insertions and deletions of elements at both ends of the list. It also allows the findmin (equivalently findmax) operation, which returns the element of least (greatest) key, but it does not allow a general delet ..."
Abstract

Cited by 15 (7 self)
 Add to MetaCart
A deque with heap order is a linear list of elements with realvalued keys which allows insertions and deletions of elements at both ends of the list. It also allows the findmin (equivalently findmax) operation, which returns the element of least (greatest) key, but it does not allow a general deletemin (deletemax) operation. Such a data structure is also called a mindeque (maxdeque) . Whereas implementing mindeques in constant time per operation is a solved problem, catenating mindeques in sublogarithmic time has until now remained open. This paper provides an efficient implementation of catenable mindeques, yielding constant amortized time per operation. The important algorithmic technique employed is an idea which is best described as data structural bootstrapping: We abstract mindeques so that their elements represent other mindeques, effecting catenation while preserving heap order. The efficiency of the resulting data structure depends upon the complexity of a special case of pa...
An Empirical Comparison of Priority Queue Algorithms
"... In the last three decades a considerable amount of research has been pursued in the efficient implementation of the pending event set (PES) associated with discreteevent simulation. The reason is simple: a fast event management has a very crucial impact in the total running time of both sequential ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
In the last three decades a considerable amount of research has been pursued in the efficient implementation of the pending event set (PES) associated with discreteevent simulation. The reason is simple: a fast event management has a very crucial impact in the total running time of both sequential and parallel simulations. This report focuses on this problem by studying the empirical performance of a number of solutions to the PES implementation in which we include a complete binary tree described in [26], 1 Introduction The PES is defined as the set of all the events generated during a discreteevent simulation and whose occurrence have not been simulated yet. In order to determine the next event to take place, it is necessary to extract the event with the least time from the PES. We call this operation extractmin. On the other hand, the occurrence of any event during the simulation can produce the insertion of new pending or future events in the PES; insert operation. These two b...
On random Cartesian trees
 Random Structures Algorithms
, 1994
"... Cartesian trees are binary search trees in which the nodes exhibit the heap property according to a second (priority) key. lithe search key and the priority key are independent, and the tree is built. based on n independent copies, Cartesian trees basically behave like ordinary random binary search ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
Cartesian trees are binary search trees in which the nodes exhibit the heap property according to a second (priority) key. lithe search key and the priority key are independent, and the tree is built. based on n independent copies, Cartesian trees basically behave like ordinary random binary search trees. In this article, we analyze the expected behavior when the keys are dependent: in most cases, the expected search, insertion, and deletion times are of). We indicate how these results can be used in the analysis of divideandconquer algorithms for maximal vectors and convex hulls. Finally, we look at distributions for which the expected time per operation grows like n a for a E [112, 1}.
Applications of Steins method in the analysis of random binary search trees. Steins method and Applications
 Institute for Mathematical Sciences Lecture Notes Series
, 2005
"... Abstract. Under certain conditions, sums of functions of subtrees of a random binary search tree are asymptotically normal. We show how Stein’s method can be applied to study these random trees, and survey methods for obtaining limit laws for such functions of subtrees. ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Abstract. Under certain conditions, sums of functions of subtrees of a random binary search tree are asymptotically normal. We show how Stein’s method can be applied to study these random trees, and survey methods for obtaining limit laws for such functions of subtrees.