Improving time and space efficiency in generalized binary search trees

Summary This paper deals with main memory data structures for which time and space performance are simultaneously considered. The main contribution is a new data structure called Generalised Binary Search Tree (GBS-tree) together with searching and updating algorithms on this structure. GBS-trees generalise different data structures based on binary trees that have appeared in the literature. A k-t GBS-tree allows up to t keys per node and subtrees in the tree's fringe of exactly 2k-1 full nodes are kept balanced. Their time and space performances are analysed in depth. The time performance is expressed in terms of the average and the variance of the number of binary comparisons between a given key and keys already in the structure. The space performance measures both the space used to space generated ratio (space utilization factor) and the pointers to keys ratio of these trees. The analysis shows that the time performance always improves when GBS-trees of higher order are considered. In the absence of balancing techniques, larger values of t, which produces smaller pointers to key ratios, induce unacceptably poor space utilizations factors. We show that both pointers to keys ratio and space utilization factor improve when larger values of k are used. Thus, local balancing techniques are adequate, not only for time performance improvement, but also, for space performance improvement.     

The analysis of Range Quickselect and related problems

Range Quickselect, a simple modification of the well-known Quickselect algorithm for selection, can be used to efficiently find an element with rank k in a given range [i..j], out of n given elements. We study basic cost measures of Range Quickselect by computing exact and asymptotic results for the expected number of passes, comparisons and data moves during the execution of this algorithm.The key element appearing in the analysis of Range Quickselect is a trivariate recurrence that we solve in full generality. The general solution of the recurrence proves to be very useful, as it allows us to tackle several related problems, besides the analysis that originally motivated us.In particular, we have been able to carry out a precise analysis of the expected number of moves of the pth element when selecting the jth smallest element with standard Quickselect, where we are able to give both exact and asymptotic results.Moreover, we can apply our general results to obtain exact and asymptotic results for several parameters in binary search trees, namely the expected number of common ancestors of the nodes with rank i and j, the expected size of the subtree rooted at the least common ancestor of the nodes with rank i and j, and the expected distance between the nodes of ranks i and j.     

A load-balanced parallel sorting algorithm for shared-nothing architectures

With the popularity of parallel database machines based on the shared-nothing architecture, it has become important to find external sorting algorithms which lead to a load-balanced computation, i.e., balanced execution, communication and output. If during the course of the sorting algorithm each processor is equally loaded, parallelism is fully exploited. Similarly, balanced communication will not congest the network traffic. Since sorting can be used to support a number of other relational operations (joins, duplicate elimination, building indexes etc.) data skew produced by sorting can further lead to execution skew at later stages of these operations. In this paper we present a load-balanced parallel sorting algorithm for shared-nothing architectures. It is a multiple-input multiple-output algorithm with four stages, based on a generalization of Batcher's odd-even merge. At each stage then keys are evenly distributed among thep processors (i.e., there is no final sequential merge phase) and the distribution of keys between stages ensures against network congestion. There is no assumption made on the key distribution and the algorithm performs equally well in the presence of duplicate keys. Hence our approach always guarantees its performance, as long asn is greater thanp ³, which is the case of interest for sorting large relations. In addition, processors can be added incrementally. Recommended by: Patrick Valduriez     

Sorting and Searching in Faulty Memories

In this paper we investigate the design and analysis of algorithms resilient to memory faults. We focus on algorithms that, despite the corruption of some memory values during their execution, are nevertheless able to produce a correct output at least on the set of uncorrupted values. In this framework, we consider two fundamental problems: sorting and searching. In particular, we prove that any O(nlog n) comparison-based sorting algorithm can tolerate the corruption of at most O((nlog n)^1/2) keys. Furthermore, we present one comparison-based sorting algorithm with optimal space and running time that is resilient to O((nlog n)^1/3) memory faults. We also prove polylogarithmic lower and upper bounds on resilient searching. This work has been partially supported by the Sixth Framework Programme of the EU under Contract Number 507613 (Network of Excellence “EuroNGI: Designing and Engineering of the Next Generation Internet”) and by MIUR, the Italian Ministry of Education, University and Research, under Project ALGO-NEXT (“Algorithms for the Next Generation Internet and Web: Methodologies, Design and Experiments”). A preliminary version of this work was presented at the 36th ACM Symposium on Theory of Computing (STOC’04) .     

Quantum Complexities of Ordered Searching, Sorting, and Element Distinctness

   Abstract. We consider the quantum complexities of the following three problems: searching an ordered list, sorting an un-ordered list, and deciding whether the numbers in a list are all distinct. Letting N be the number of elements in the input list, we prove a lower bound of (1/π )(ln(N )-1) accesses to the list elements for ordered searching, a lower bound of Ω(N logN ) binary comparisons for sorting, and a lower bound of

Towards a Realistic Analysis of the QuickSelect Algorithm

We revisit the analysis of the classical QuickSelect algorithm. Usually, the analysis deals with the mean number of key comparisons, but here we view keys as words produced by a source, and words are compared via their symbols in lexicographic order. Our probabilistic models belong to a broad category of information sources that encompasses memoryless (i.e., independent-symbols) and Markov sources, as well as many unbounded-correlation sources. The “realistic” cost of the algorithm is here the total number of symbol comparisons performed by the algorithm, and, in this context, the average-case analysis aims to provide estimates for the mean number of symbol comparisons. For the QuickSort algorithm, known average-case complexity results are of \({\Theta } (n \log n)\) in the case of key comparisons, and \({\Theta }(n\log ^{2} n)\) for symbol comparisons. For QuickSelect algorithms, and with respect to key comparisons, the average-case complexity is Θ(n). In this present article, we prove that, with respect to symbol comparisons, QuickSelect’s average-case complexity remains Θ(n). In each case, we provide explicit expressions for the dominant constants, closely related to the probabilistic behaviour of the source.     

Rainbow Sort: Sorting at the Speed of Light

Rainbow Sort is an unconventional method for sorting, which is based on the physical concepts of refraction and dispersion. It is inspired by the observation that light that traverses a prism is sorted by wavelength. At first sight this “rainbow effect” that appears in nature has nothing to do with a computation in the classical sense, still it can be used to design a sorting method that has the potential of running in Θ (n) with a space complexity of Θ (n), where n denotes the number of elements that are sorted. In Section 1, some upper and lower bounds for sorting are presented in order to provide a basis for comparisons. In Section 2, the physical background is outlined, the setup and the algorithm are presented and a lower bound for Rainbow Sort of Ω (n) is derived. In Section 3, we describe essential difficulties that arise when Rainbow Sort is implemented. Particularly, restrictions that apply due to the Heisenberg uncertainty principle have to be considered. Furthermore, we sketch a possible implementation that leads to a running time of O(n+m), where m is the maximum key value, i.e., we assume that there are integer keys between 0 and m. Section 4 concludes with a summary of the complexity and some remarks on open questions, particularly on the treatment of duplicates and the preservation of references from the keys to records that contain the actual data. In Appendix A, a simulator is introduced that can be used to visualise Rainbow Sort.     

The effect of multiple reference characters on detecting matches in string‐searching algorithms

The effect of multiple reference characters and the condition types on the performance of exact string‐searching algorithms is tested. In order to perform such a test a new algorithm called the Multiple Reference Characters Algorithm (MRCA) is developed. An experiment is performed using English text; the results are compared with the known string‐matching algorithms called Boyer–Moore–Horspool (BMH) and Straight Forward (Naïve). With the MRCA algorithm, the shift distance is increased up to 3m + 1 positions in comparison with exactly one position in the Naïve algorithm and up to m positions in BMH. Furthermore, by using the new algorithm MRCA, the results suggest that the evaluation criteria of the average number of comparisons, the average number of shifts, and the clock time required by BMH are improved up to 73.1%, 64.7%, and 49.6%, respectively. The same evaluation criteria required by Naïve are improved by MRCA up to 98.1%, 98%, and 94.7%, respectively. Copyright © 2005 John Wiley & Sons, Ltd.     

On-Line Approximate String Searching Algorithms: Survey and Experimental Results

The problem of approximate string searching comprises two classes of problems: string searching with k mismatches and string searching with k differences. In this paper we present a short survey and experimental results for well known sequential approximate string searching algorithms. We consider algorithms based on different approaches including dynamic programming, deterministic finite automata, filtering, counting and bit parallelism. We compare these algorithms in terms of running time against pattern length and for several values of k for four different kinds of text: binary alphabet, alphabet of size 8, English alphabet and DNA alphabet. Finally, we compare the experimental results of the algorithms with their theoretical complexities.     

Optimal binary search trees with costs depending on the access paths

We describe algorithms for constructing optimal binary search trees, in which the access cost of a key depends on the k preceding keys which were reached in the path to it. This problem has applications to searching on secondary memory and robotics. Two kinds of optimal trees are considered, namely optimal worst case trees and weighted average case trees. The time and space complexities of both algorithms are O(n^k+2) and O(n^k+1), respectively. The algorithms are based on a convenient decomposition and characterizations of sequences of keys which are paths of special kinds in binary search trees. Finally, using generating functions, we present an exact analysis of the number of steps performed by the algorithms.     

The analysis of Range Quickselect and related problems

Range Quickselect, a simple modification of the well-known Quickselect algorithm for selection, can be used to efficiently find an element with rank k in a given range [i..j], out of n given elements. We study basic cost measures of Range Quickselect by computing exact and asymptotic results for the expected number of passes, comparisons and data moves during the execution of this algorithm.The key element appearing in the analysis of Range Quickselect is a trivariate recurrence that we solve in full generality. The general solution of the recurrence proves to be very useful, as it allows us to tackle several related problems, besides the analysis that originally motivated us.In particular, we have been able to carry out a precise analysis of the expected number of moves of the pth element when selecting the jth smallest element with standard Quickselect, where we are able to give both exact and asymptotic results.Moreover, we can apply our general results to obtain exact and asymptotic results for several parameters in binary search trees, namely the expected number of common ancestors of the nodes with rank i and j, the expected size of the subtree rooted at the least common ancestor of the nodes with rank i and j, and the expected distance between the nodes of ranks i and j.     

New method in information processing for maintaining an efficient dynamic ordered set

This paper investigates how to maintain an efficient dynamic ordered set of bit strings, which is an important problem in the field of information search and information processing. Generally, a dynamic ordered set is required to support 5 essential operations including search, insertion, deletion, max-value retrieval and next-larger-value retrieval. Based on previous research fruits, we present an advanced data structure named rich binary tree (RBT), which follows both the binary-search-tree property and the digital-search-tree property. Also, every key K keeps the most significant difference bit (MSDB) between itself and the next larger value among K’s ancestors, as well as that between itself and the next smaller one among its ancestors. With the new data structure, we can maintain a dynamic ordered set in O(L) time. Since computers represent objects in binary mode, our method has a big potential in application. In fact, RBT can be viewed as a general-purpose data structure for problems concerning order, such as search, sorting and maintaining a priority queue. For example, when RBT is applied in sorting, we get a linear-time algorithm with regard to the key number and its performance is far better than quick-sort. What is more powerful than quick-sort is that RBT supports constant-time dynamic insertion/deletion. Supported by the National Natural Science Foundation of China (Grant No. 60873111), and the National Basic Research Program of China (Grant No. 2004CB719400)     

Randomized selection in n+C+o(n) comparisons

In this paper we present a randomized selection algorithm that with high probability 1−1/n^ρ, for any constant ρ>1 requires n+C+o(n) comparisons to determine the Cth order statistic of n keys thus matching a corresponding lower bound on the average number of comparisons required. Low order terms in the number of comparisons performed can also be reduced by extending the algorithm of Floyd and Rivest and analyzing its resulting performance more carefully.     

Spin-the-Bottle Sort and Annealing Sort: Oblivious Sorting via Round-Robin Random Comparisons

We study sorting algorithms based on randomized round-robin comparisons. Specifically, we study Spin-the-bottle sort, where comparisons are unrestricted, and Annealing sort, where comparisons are restricted to a distance bounded by a temperature parameter. Both algorithms are simple, randomized, data-oblivious sorting algorithms, which are useful in privacy-preserving computations, but, as we show, Annealing sort is much more efficient. We show that there is an input permutation that causes Spin-the-bottle sort to require Ω(n ²logn) expected time in order to succeed, and that in O(n ²logn) time this algorithm succeeds with high probability for any input. We also show there is a specification of Annealing sort that runs in O(nlogn) time and succeeds with very high probability.     

Implementation and analysis of optimized architectures for rank order filter

In this paper we present two architectures based on the replication sort algorithm (RSA) and rank based network sorting algorithm (RBNS) for implementation of Rank order filer (ROF). This paper focuses on optimization strategies for sorting in terms of operating speed (throughput) and area (no. of comparators). The RSA algorithm achieves maximum throughput by sorting, which finds the position of all the window elements in parallel using eight bit comparators, a LUT to store the bit sum and a decoder. The time cost for filtering the complete image remains constant irrespective of the size of the window and the algorithm is generalized for all rank orders. The RBNS architecture is based on Sorting Network architecture algorithm, optimized for each desired output rank with O (N) hardware complexity compared to O (N²) complexity of the existing architectures that are based on bubble-sort and quick-sort reported so far. The proposed architectures use the concepts of pipelining and grain level parallelism and accomplish the task of sorting and filtering each sample appearing at the input window of the filter in one clock cycle, excluding the initial latency.     

A note on searching in a binary search tree

An algorithm for searching in a binary search tree using two-way comparisons is presented. The number of comparisons required by this algorithm is only one more than when using three-way comparisons. Since most high-level programming languages do not supply three-way comparisons, the number of comparisons used de facto is reduced by a factor of two. We give experimental results to indicate the speed-up that may be achieved by the presented algorithm.     

COMPUTATIONAL METHODS THROUGH GENETIC ALGORITHMS FOR OBTAINING STACKELBERG SOLUTIONS TO TWO-LEVEL INTEGER PROGRAMMING PROBLEMS

ABSTRACT

In this article, we present a method for obtaining Stackelberg solutions to two-level integer problems through genetic algorithms, which have received much attention as a promising computational method for complex problems. Assuming that there exist the upper- and the lower-bounds constraints with respect to integer variables, we employ a zero-one bit string as an individual in our artificial genetic systems. It is required that each individual satisfies the constraints of the given problem and a response of the lower level decision maker with respect to a decision that the upper level decision maker is rational. Therefore, individuals not satisfying the two conditions are penalized in the artificial genetic systems. To demonstrate the feasibility and efficiency of the proposed methods, computational experiments are carried out and comparisons between the Moore and Bard method based on the branch-and-bound techniques and the proposed methods are provided.     

Exact Schema Theory for Genetic Programming and Variable-Length Genetic Algorithms with One-Point Crossover

A few schema theorems for genetic programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rather than an exact value. This paper presents theoretical results for GP with one-point crossover which overcome this problem. First, we give an exact formulation for the expected number of instances of a schema at the next generation in terms of microscopic quantities. Due to this formulation we are then able to provide an improved version of an earlier GP schema theorem in which some (but not all) schema creation events are accounted for. Then, we extend this result to obtain an exact formulation in terms of macroscopic quantities which makes all the mechanisms of schema creation explicit. This theorem allows the exact formulation of the notion of effective fitness in GP and opens the way to future work on GP convergence, population sizing, operator biases, and bloat, to mention only some of the possibilities.     

An efficient key management scheme for user access control in outsourced databases

Recently, researches on key management scheme for user access control in outsourced databases have been actively done. Because outsourced databases require dealing with a lot of users and data resources, an efficient key management scheme for reducing the number of authentication keys is required. However, the existing schemes have a critical problem that the cost of key management is rapidly increasing as the number of keys becomes larger. To solve the problem, we propose an efficient key management scheme for user access control in outsourced databases. For this, we propose an Resource Set Tree(RST)-based key generation algorithm to reduce key generation cost by merging duplicated data resources. In addition, we propose a hierarchical Chinese Remainder Theorem(CRT)-based key assignment algorithm which can verify a user permission to gain accesses to outsourced databases. Our algorithm can reduce key update cost because the redistribution of authentication keys is not required. We also provide the analytic cost models of our algorithms and verify the correctness of the theoretical analysis by comparing them with experiment results. Finally, we show from the performance analysis that the proposed scheme outperforms the existing schemes in terms of both key generation cost and update cost.     

Efficient out-of-core sorting algorithms for the Parallel Disks Model

In this paper,¹ we present efficient algorithms for sorting on the Parallel Disks Model (PDM). Numerous asymptotically optimal algorithms have been proposed in the literature. However, many of these merge based algorithms have large underlying constants in the time bounds, because they suffer from the lack of read parallelism on the PDM. The irregular consumption of the runs during the merge affects the read parallelism and contributes to the increased sorting time. In this paper, we first introduce a novel idea called the dirty sequence accumulation that improves the read parallelism. Next, we show analytically that this idea can reduce the number of parallel I/O’s required to sort the input close to the lower bound of . We verify experimentally our dirty sequence idea with the standard R-Way merge and show that our idea can reduce the number of parallel I/Os to sort on the PDM significantly.