Abstract—Computational problems have significance from the early civilizations. These problems and solutions are used for the study of universe. Numbers and symbols have been used for different fields e.g. mathematics, statistics. After the emergence of computers the number and objects needs to be arranged in a particular order i.e. ascending and descending orders. The ordering of these numbers is generally referred to as sorting. Sorting gained a lot of importance in computer sciences and its applications are in file systems etc. A number of sorting algorithms have been proposed with different time and space complexities. In this paper author will propose a new sorting algorithm i.e. Relative Split and Concatenate Sort, implement the algorithm and then compared results with some of the existing sorting algorithms. Algorithm’s time and space complexity will also be the part of this paper.
Keywords: New Sorting, Time Complexity, RSCS.
I. INTRODUCTION
Sorting gained a lot of importance in computer sciences and its applications are in file systems, sequential and multiprocessing computing, and a core part of database systems. A number of sorting algorithms have been proposed with different time and space complexities. There is no one sorting algorithm that is best for each and every situation. Donald Knuth in [1] reports that “computer manufacturers of the 1960s estimated that more than 25 percent of the running time on their computers was spend on sorting, when all their customers were taken into account. In fact, there were many installations in which the task of sorting was responsible for more than half of the computing time.” Sorting is a significant concept whenever we study algorithms. Knuth divides the taxonomy of sorting...
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0.1 abstract In a graph theory the shortest path problem is nding a minimum path and distance between two vertices. The ap- plication in many areas of shortest path algorithms are such as geographical rout- ing, transportation, computer vision and VLSI design involve solving optimiza- tion problems on large planar graphs. To calculate the shortest path we need to know some algorithms like Kruskal's algorithm,Prim's algorithm,Dijkstra's algorithm,BellmanFord's algorithm.
Introduction to the basic concepts of probability and statistics with discussion of applications to computer science.
* Question 1. Write pseudocode and a diagram that shows how to implement the merge part of the merge-sort algorithm using two stacks (one for each subsequence), and be sure to use the correct ADT operations for stacks. Do not write Java code, or pseudocode for merge-sort.
If we take this in a practical point of view B-Tree offers you a guarantee an access time of less than 10ms even for extremely large datasets.
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There are several basic and advance sorting algorithms. All sorting algorithm apply to specific quite issues. One among the basic issues of computer science is ordering an inventory of things. There is a plethora of solutions to this problem, referred to as sorting algorithms. Some sorting algorithms are simple and intuitive, such as the bubble sort. Others, such as the quick sort are extraordinarily sophisticated, however turnout lightning-fast results. The common sorting algorithms will be divided into two categories by the complexity of their algorithms ( Deepak Garg 2009). There is an immediate correlation between the complexity of an algorithm and its relative efficiency. Algorithmic complexity is usually written in a very kind referred to as Big-O notation, wherever the O represents the complexity of the algorithm and a value n represents the size of the set the algorithm is run against. The two categories of sorting algorithms are O(n2), which incorporates the bubble, insertion, selection, and shell, sorts; and O(n log n) which incorporates the heap, merge, and quick sort.
As we all know that Exascale computers runs million processors which generates data at a rate of terabytes per second. It is impossible to store data generated at such a rate. Methods like dynamic reduction of data by summarization, subset selection, and more sophisticated dynamic pattern identification methods will be necessary to reduce the volume of data. And also the reduced volume needs to be stored at the same rate which it is generated in order to proceed without interruption. This requirement will present new challenges for the movement of data from one super computer to the local and remote storage systems. Data distribution have to be integrated into the data generation phase. This issue of large scale data movement will become more acute as very large datasets and subsets are shared by large scientific communities, this situation requires a large amount of data to be replicated or moved from production to the analysis machines which are sometimes in wide area. While network technology is greatly improved with the introduction of optical connectivity the transmission of large volumes of data will encounter transient failure and automatic recovery tools will be necessary. Another fundamental requirement is the automatic allocation, use and release of storage space. Replicated data cannot be left
Paging is one of the memory-management schemes by which a computer can store and retrieve data from secondary storage for use in main memory. Paging is used for faster access to data. The paging memory-management scheme works by having the operating system retrieve data from the secondary storage in same-size blocks called pages. Paging writes data to secondary storage from main memory and also reads data from secondary storage to bring into main memory. The main advantage of paging over memory segmentation is that is allows the physical address space of a process to be noncontiguous. Before paging was implemented, systems had to fit whole programs into storage, contiguously, which would cause various storage problems and fragmentation inside the operating system (Belzer, Holzman, & Kent, 1981). Paging is a very important part of virtual memory impl...
The Euclidean algorithm is described in two books of the Elements, VII (7) and X (10), and it discusses the computing of the greatest common factor of two positive intege...
9 Fayyad U., Piatetsky-Shapiro G., Smyth, Padhraic - "The KDD Process for Extracting Useful Knowledge from volumes of Data" - Communications of the ACM vol. 39, no. 11 (Nov. 1996).
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
Graph theory has a wide range of applications as we have discovered. These have ranged from the famous Leonhard Euler’s solving of The Seven Bridges of Königsberg problem, to the classic four color theorem, and finally to the current focuses on applications within the realm of computer and data science. With all of these uses, it is certainly clear that graph theory is a subject of modern mathematics that is here to stay. Not only are there enormous applications to a large number of fields, but graph theory does a tremendous job of modeling, explaining, and solving real world problems.
... al. (2011) gives a mixed integer programming (MIP) method which is useful for constructing orthogonal designs.
Definition 12 : School bus routing problems is a finite ordered pair of elements (G;g;w; f )