Sorting an array of objects such as integers, bytes, floats, etc is considered as one of the most important problems in Computer Science. Quicksort is an effective and wide studied sorting algorithm to sort an array o...Sorting an array of objects such as integers, bytes, floats, etc is considered as one of the most important problems in Computer Science. Quicksort is an effective and wide studied sorting algorithm to sort an array of n distinct elements using a single pivot. Recently, a modified version of the classical Quicksort was chosen as standard sorting algorithm for Oracles Java 7 routine library due to Vladimir Yaroslavskiy. The purpose of this paper is to present the different behavior of the classical Quicksort and the Dual-pivot Quicksort in complexity. In Particular, we discuss the convergence of the Dual-pivot Quicksort process by using the contraction method. Moreover we show the distribution of the number of comparison done by the duality process converges to a unique fixed point.展开更多
In this paper, we study a new version from Dual-pivot Quicksort algorithm when we have some other number of pivots. Hence, we discuss the idea of picking pivots ?by random way and splitting the list simultaneously acc...In this paper, we study a new version from Dual-pivot Quicksort algorithm when we have some other number of pivots. Hence, we discuss the idea of picking pivots ?by random way and splitting the list simultaneously according to these. The modified version generalizes these results for multi process. We show that the average number of swaps done by Multi-pivot Quicksort process and we present a special case. Moreover, we obtain a relationship between the average number of swaps of Multi-pivot Quicksort and Stirling numbers of the first kind.展开更多
With the development and progress of today’s network information technology,a variety of large-scale network databases have emerged with the situation,such as Baidu Library and Weipu Database,the number of documents ...With the development and progress of today’s network information technology,a variety of large-scale network databases have emerged with the situation,such as Baidu Library and Weipu Database,the number of documents in the inventory has reached nearly one million.So how do you quickly and effectively retrieve the information you want in such a huge database?This requires finding efficient algorithms to reduce the computational complexity of the computer during Information Retrieval,improve retrieval efficiency,and adapt to the rapid expansion of document data.The Quicksort Algorithm gives different weights to each position of the document,and multiplies the weight of each position with the number of matches of that position,and then adds all the multiplied sums to set a feature value for Quicksort,which can achieve the full accuracy of Information Retrieval.Therefore,the purpose of this paper is to use the quick sort algorithm to increase the speed of Information Retrieval,and to use the position weighting algorithm to improve the matching quality of Information Retrieval,so as to achieve the overall effect of improving the efficiency of Information Retrieval.展开更多
This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison ...This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases.展开更多
文摘Sorting an array of objects such as integers, bytes, floats, etc is considered as one of the most important problems in Computer Science. Quicksort is an effective and wide studied sorting algorithm to sort an array of n distinct elements using a single pivot. Recently, a modified version of the classical Quicksort was chosen as standard sorting algorithm for Oracles Java 7 routine library due to Vladimir Yaroslavskiy. The purpose of this paper is to present the different behavior of the classical Quicksort and the Dual-pivot Quicksort in complexity. In Particular, we discuss the convergence of the Dual-pivot Quicksort process by using the contraction method. Moreover we show the distribution of the number of comparison done by the duality process converges to a unique fixed point.
文摘In this paper, we study a new version from Dual-pivot Quicksort algorithm when we have some other number of pivots. Hence, we discuss the idea of picking pivots ?by random way and splitting the list simultaneously according to these. The modified version generalizes these results for multi process. We show that the average number of swaps done by Multi-pivot Quicksort process and we present a special case. Moreover, we obtain a relationship between the average number of swaps of Multi-pivot Quicksort and Stirling numbers of the first kind.
基金This work was supported in part by the National Natural Science Foundation of China,Grant No.72073041Open Foundation for the University Innovation Platform in the Hunan Province,Grant No.18K103.2011+2 种基金Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property.Hunan Provincial Key Laboratory of Finance&Economics Big Data Science and Technology2020 Hunan Provincial Higher Education Teaching Reform Research Project under Grant HNJG-2020-1130,HNJG-2020-11242020 General Project of Hunan Social Science Fund under Grant 20B16.
文摘With the development and progress of today’s network information technology,a variety of large-scale network databases have emerged with the situation,such as Baidu Library and Weipu Database,the number of documents in the inventory has reached nearly one million.So how do you quickly and effectively retrieve the information you want in such a huge database?This requires finding efficient algorithms to reduce the computational complexity of the computer during Information Retrieval,improve retrieval efficiency,and adapt to the rapid expansion of document data.The Quicksort Algorithm gives different weights to each position of the document,and multiplies the weight of each position with the number of matches of that position,and then adds all the multiplied sums to set a feature value for Quicksort,which can achieve the full accuracy of Information Retrieval.Therefore,the purpose of this paper is to use the quick sort algorithm to increase the speed of Information Retrieval,and to use the position weighting algorithm to improve the matching quality of Information Retrieval,so as to achieve the overall effect of improving the efficiency of Information Retrieval.
文摘This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases.
