We propose a version of cache oblivious search trees which is simpler than the previous proposal of Bender, Demaine and Farach-Colton and has the same complexity bounds. In particular, our data structure avoids the use of weight balanced B-trees, and can be implemented as just a single array of data elements, without the use of pointers. The structure also improves space utilization.

. A priority queue Q is a data structure that maintains a collection of elements, each element having an associated priority drawn from a totally ordered universe, under the operations Insert, which inserts an element into Q, and DeleteMin, which deletes an element with the minimum priority from Q. In this paper a priority-queue implementation is given which is efficient with respect to the number of block transfers or I/Os performed between the internal and external memories of a computer. Let B and M denote the respective capacity of a block and the internal memory measured in elements. The developed data structure handles any intermixed sequence of Insert and DeleteMin operations such that in every disjoint interval of B consecutive priorityqueue operations at most c log M=B N M I/Os are performed, for some positive constant c. These I/Os are divided evenly among the operations: if B c log M=B N M , one I/O is necessary for every B=(c log M=B N M )th operation ...

An implementation of priority queues is presented that supports the operations MakeQueue, FindMin, Insert, Meld and DecreaseKey in worst case time O(1) and DeleteMin and Delete in worst case time O(log n). The space requirement is linear. The data structure presented is the first achieving this worst case performance. 1 Introduction We consider the problem of implementing priority queues which are efficient in the worst case sense. The operations we want to support are the following commonly needed priority queue operations [11]. MakeQueue creates and returns an empty priority queue. FindMin(Q) returns the minimum element contained in priority queue Q. Insert(Q; e) inserts an element e into priority queue Q. Meld(Q 1 ; Q 2 ) melds priority queues Q 1 and Q 2 to a new priority queue and returns the resulting priority queue. DecreaseKey(Q; e; e 0 ) replaces element e by e 0 in priority queue Q provided e 0 e and it is known where e is stored in Q. DeleteMin(Q) deletes and...

In the stable 0-1 sorting problem the task is to sort an array of n elements with two distinct values such that equal elements retain their relative input order.

We present priority queues that support the operations MakeQueue, FindMin, Insert and Meld in worst case time O(1) and Delete and DeleteMin in worst case time O(log n). They can be implemented on the pointer machine and require linear space. The time bounds are optimal for all implementations where Meld takes worst case time o(n).

Abstract. We introduce a framework for reducing the number of element comparisons performed in priority-queue operations. In particular, we give a priority queue which guarantees the worst-case cost of O(1) per minimum finding and insertion, and the worst-case 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 above-mentioned methods, the priority-decrease (or decrease-key) method. This priority queue achieves the worst-case cost of O(1) per minimum finding, insertion, and priority decrease; and the worst-case 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

Abstract. Colored range queries are a well-studied topic in computational geometry and database research that, in the past decade, have found exciting applications in information retrieval. In this paper we give improved time and space bounds for three important one-dimensional colored range queries — colored range listing, colored range top-k queries and colored range counting — and, thus, new bounds for various document retrieval problems on general collections of sequences. Specifically, we first describe a framework including almost all recent results on colored range listing and document listing, which suggests new combinations of data structures for these problems. For example, we give the fastest compressed data structures for colored range listing and document listing, and an efficient data structure for document listing whose size is bounded in terms of the high-order entropies of the library of documents. We then show how (approximate) colored top-k queries can be reduced to (approximate) range-mode queries on subsequences, yielding the first efficient data structure for this problem. Finally, we show how a modified wavelet tree can support colored range counting in logarithmic time and space that is succinct whenever the number of colors is superpolylogarithmic in the length of the sequence. 1

This paper explains binomial heaps, a beautiful data structure for priority queues, using the functional programming language Haskell (Peterson & Hammond, 1997). We largely follow a deductive approach: using the metaphor of a tennis tournament we show that binomial heaps arise naturally through a number of logical steps. Haskell supports the deductive style of presentation very well: new types are introduced at ease, algorithms can be expressed clearly and succinctly, and Haskell's type classes allow to capture common algorithmic patterns. The paper aims at the level of an undergraduate student who has experience in reading and writing Haskell programs, and who is familiar with the concept of a priority queue. 2 Priority queues

Abstract. Two new ways of transforming a priority queue into a double-ended priority queue are introduced. These methods can be used to improve all known bounds for the comparison complexity of double-ended priority-queue operations. Using an efficient priority queue, the first transformation can produce a doubleended priority queue which guarantees the worst-case cost of O(1) for find-min, find-max, and insert; and the worst-case cost of O(lg n) including at most lg n + O(1) element comparisons for delete, but the data structure cannot support meld efficiently. Using a meldable priority queue that supports decrease efficiently, the second transformation can produce a meldable double-ended priority queue which guarantees the worst-case cost of O(1) for find-min, find-max, and insert; the worst-case cost of O(lg n) including at most lg n + O(lg lg n) element comparisons for delete; and the worst-case cost of O(min {lg m, lg n}) for meld. Here, m and n denote the number of elements stored in the data structures prior to the operation in question, and lg n is a shorthand for log 2 (max {2, n}). 1.

. The complexity of maintaining a set under the operations Insert, Delete and FindMin is considered. In the comparison model it is shown that any randomized algorithm with expected amortized cost t comparisons per Insert and Delete has expected cost at least n=(e2 2t ) \Gamma 1 comparisons for FindMin. If FindMin is replaced by a weaker operation, FindAny, then it is shown that a randomized algorithm with constant expected cost per operation exists; in contrast, it is shown that no deterministic algorithm can have constant cost per operation. Finally, a deterministic algorithm with constant amortized cost per operation for an offline version of the problem is given. CR Classification: F.2.2 1. Introduction We consider the complexity of maintaining a set S of elements from a totally ordered universe under the following operations: Insert(x): inserts the element x into S, Delete(x): removes from S the element x provided it is known where x is stored, and Supported by the Danish...