This paper advocates the adaption of external memory algorithms to this purpose. This idea and the practical issues involved are exemplified by engineering a fast priority queue suited to external memory and cached memory that is based on k-way merging. It improves previous external memory algorithms by constant factors crucial for transferring it to cached memory. Running in the cache hierarchy of a workstation the algorithm is at least two times faster than an optimized implementation of binary heaps and 4-ary heaps for large inputs

In this paper we systematically derive randomized algorithms (both sequential and parallel) for sorting and selection from basic principles and fundamental techniques like random sampling. We prove several sampling lemmas which will find independent applications. The new algorithms derived here are the most efficient known. From among other results, we have an efficient algorithm for sequential sorting. The problem of sorting has attracted so much attention because of its vital importance. Sorting with as few comparisons as possible while keeping the storage size minimum is a long standing open problem. This problem is referred to as ‘the minimum storage sorting ’ [10] in the literature. The previously best known minimum storage sorting algorithm is due to Frazer and McKellar [10]. The expected number of comparisons made by this algorithm is n log n + O(n log log n). The algorithm we derive in this paper makes only an expected n log n + O(n ω(n)) number of comparisons, for any function ω(n) that tends to infinity. A variant of this algorithm makes no more than n log n + O(n log log n) comparisons on any input of size n with overwhelming probability. We also prove high probability bounds for several randomized algorithms for which only expected bounds have been proven so far.

Let p; q; r; s be polynomials with integer coecients. This paper presents a fast method, using very little temporary storage, to nd all small integers (a; b; c; d) satisfying p(a)+q(b) = r(c)+s(d). Numerical results include all small solutions to a ; all small solutions to a ; and the smallest positive integer that can be written in 5 ways as a sum of two coprime cubes.

Two in-place variants of the classical mergesort algorithm are analysed in detail. The first, straightforward variant performs at most N log 2 N + O(N ) comparisons and 3N log 2 N + O(N ) moves to sort N elements. The second, more advanced variant requires at most N log 2 N + O(N ) comparisons and "N log 2 N moves, for any fixed " ? 0 and any N ? N ("). In theory, the second one is superior to advanced versions of heapsort. In practice, due to the overhead in the index manipulation, our fastest in-place mergesort behaves still about 50 per cent slower than the bottom-up heapsort. However, our implementations are practical compared to mergesort algorithms based on in-place merging. Key words: sorting, mergesort, in-place algorithms CR Classification: F.2.2 1.

The problem of Similarity Searching consists in finding the elements from a set which are similar to a given query under some criterion. If the similarity is expressed by means of a metric, the problem is called Metric Space Searching. In this thesis we present new methodologies to solve this problem using graphs G(V,E) to represent the metric database. In G, the set V corresponds to the objects from the metric space and E to a small subset of edges from V × V, whose weights are computed according to the metric of the space under consideration. In particular, we study k-nearest neighbor graphs (knngs). The knng is a weighted graph connecting each element from V —or equivalently, each object from the metric space — to its k nearest neighbors. We develop algorithms both to construct knngs in general metric spaces, and to use

Let A be a set of size m. Obtaining the first k ≤ m elements of A in ascending order can be done in optimal O(m+k log k) time. We present an algorithm (online on k) which incrementally gives the next smallest element of the set, so that the first k elements are obtained in optimal time for any k. We also give a practical algorithm with the same complexity on average, which improves in practice the existing online algorithm. As a direct application, we use our technique to implement Kruskal’s Minimum Spanning Tree algorithm, where our solution is competitive with the best current implementations. We finally show that our technique can be applied to several other problems, such as obtaining an interval of the sorted sequence and implementing heaps. 1

First we present a new variant of Merge-sort, which needs only 1.25n space, because it uses space again, which becomes available within the current stage. It does not need more comparisons than classical Merge-sort. The main result is an easy to implement method of iterating the procedure in-place starting to sort 4/5 of the elements. Hereby we can keep the additional transport costs linear and only very few comparisons get lost, so that n log n − 0.8n comparisons are needed. We show that we can improve the number of comparisons if we sort blocks of constant length with Merge-Insertion, before starting the algorithm. Another improvement is to start the iteration with a better version, which needs only (1+ε)n space and again additional O(n) transports. The result is, that we can improve this theoretically up to n log n − 1.3289n comparisons in the worst case. This is close to the theoretical lower bound of n log n − 1.443n. The total number of transports in all these versions can be reduced to ε n log n+O(1) for any ε> 0. 1

Heaps constitute a well-known data structure allowing the implementation of an e#cient O(n log n) sorting algorithm as well as the design of fast priority queues. Although heaps have been known for long, their combinatorial properties are still partially worked out: exact summation formulae have been stated, but most of the asymptotic behaviors are still unknown. In this paper, we present a number of general (not restricting to special subsequences) asymptotic results that give insight on the di#culties encountered in the asymptotic study of the number of heaps of a given size and of the cost of heap construction. In particular we exhibit the influence of arithmetic functions in the apparently chaotic behavior of these quantities. It is also shown that the distribution function of the cost of heap construction using Floyd's algorithm and other variants is asymptotically normal. 1

. A variant of Heapsort---named Ultimate Heapsort---is presented that sorts n elements in-place in \Theta(n log 2 (n+ 1)) worst-case time by performing at most n log 2 n + \Theta(n) key comparisons and n log 2 n + \Theta(n) element moves. The secret behind Ultimate Heapsort is that it occasionally transforms the heap it operates with to a two-layer heap which keeps small elements at the leaves. Basically, Ultimate Heapsort is like Bottom-Up Heapsort but, due to the two-layer heap property, an element taken from a leaf has to be moved towards the root only O(1) levels, on an average. Let a[1::n] be an array of n elements each consisting of a key and some information associated with this key. This array is a (maximum) heap if, for all i 2 f2; : : : ; ng, the key of element a[bi=2c] is larger than or equal to that of element a[i]. That is, a heap is a pointer-free representation of a left complete binary tree, where the elements stored are partially ordered according to their keys. Ele...

. Dutton #1993# presents a further HEAPSORT variant called WEAK-HEAPSORT, which also contains a new data structure for priority queues. The sorting algorithm and the underlying data structure are analyzed showing that WEAK-HEAPSORT is the best HEAPSORT variant and that it has a lot of nice properties. It is shown that the worst case number of comparisons is ndlog ne# 2 dlog ne + n #dlog ne#nlog n +0:1nand weak heaps can be generated with n # 1 comparisons. A double-ended priority queue based on weakheaps can be generated in n + dn=2e#2 comparisons. Moreover, examples for the worst and the best case of WEAK-HEAPSORT are presented, the number of Weak-Heaps on f1;:::;ng is determined, and experiments on the average case are reported. 1