Counting Sort Algorithm Research Articles

Incentive Mechanism Design for Mitigating Frontrunning and Transaction Reordering in Decentralized Exchanges

Decentralized exchanges (DEXes) provide effective price discovery and fair trading while dealing with the drawbacks of centralized exchanges, e.g., lack of transaction transparency and exclusive control of user assets and transaction fees. However, many DEXes suffer from frontrunning and transaction reordering, which fundamentally flaw their design. In this paper, we present a novel incentive mechanism design for mitigating frontrunning and transaction reordering even if frontrunners pay high transaction fees in DEXes. We utilize a weighted counting sort algorithm to order transactions based on the users’ multi-dimensional private information (e.g., transaction delay and confidentiality). To elicit users’ private information, we consider a multi-dimensional contract-theoretic design based on the users’ willingness to share their private information. We show that the miner can always maximize its utility under the complete and incomplete information scenarios. We implement solutions to our multi-dimensional contract and sorting algorithm on a decentralized oracle network to create a decentralized system and design a web application to extensively evaluate the performance of our proposed incentive mechanism. We further show that ordering transactions based on users’ private information increases the miner’s utility by 78.42%-84.57% and reduces the users’ cost by 64.47% compared with the state-of-the-art fair sequencing services, automated arbitrage market maker, and miner extractable value auctions.

A fast and efficient tool to study the rheology of dense suspensions

A cutting-edge software that adopts an optimized searching algorithm is presented to tackle the Newton–Euler equations governing the dynamics of dense suspensions in Newtonian fluids. In particular, we propose an implementation of a fixed-radius near neighbors search based on an efficient counting sort algorithm with an improved symmetric search. The adopted search method drastically reduces the computational cost and allows an efficient parallelization even on a single node through the multi-threading paradigm. Emphasis is also given to the memory efficiency of the code since the history of the contacts among particles has to be traced to model the frictional contributions, when dealing with dense suspensions of rheological interest that consider non-smooth interacting particles. An effective procedure based on an estimate of the maximum number of the smallest particles surrounding the largest one (given the radii distribution) and a sort applied only to the surrounding particles only is implemented, allowing us to effectively tackle the rheology of non-monodispersed particles with a high size-ratio in large domains. Finally, we present validations and verification of the numerical procedure, by comparing with previous simulations and experiments, and present new software capabilities.

Optimized Parallel Counting Sort Algorithm for Distinct Numeric Values on Biswapped Hyper Hexa-Cell Optoelectronic Network

The optoelectronic Biswapped-Hyper Hexa-Cell is a recently reported recursive and a symmetrical architecture of Biswapped Family. This symmetrical network has claimed and proved to be advantageous in terms of network diameter, bisection width, minimum node-degree and network cost compared to its counterpart architecture of OTIS family named ‘OTIS Hyper Hexa-Cell’ and traditional grid-based architecture of Biswapped family named ‘Biswapped-Mesh’. In this paper, we present a novel and efficient parallel algorithm for counting sort for sorting distinct numeric values on dh-dimensional Biswapped-Hyper Hexa-Cell optoelectronic network. The parallel algorithm demands 10d_h+12+ log_2⁡〖S_A 〗 electronic and 10 optical moves, where SA is the size of count array: Acip[SA], and SA equals to maximal minus minimal numeric value plus one. On the basis of analysis, it is concluded that proposed algorithm delivers better performance since speedup and efficiency improved for worst case scenario (difference between maximal and minimal data values becomes larger) with the increase of only few communication moves required for sorting.

Bounds on Complexity when Sorting Reals

We derive the upper bounds on the complexity of the counting sort algorithm applied to reals. We show that the algorithm has a time complexity O(n) for n data items distributed uniformly or exponentially. The proof is based on the fact that the use of comparison-type sorting for small portion of a given data set is bounded by a linear function of n. Some numerical demonstrations are discussed.

A Novel Sorting Method for Real and Integer Numbers: An Extension of Counting Sort

Sorting is an essential conceptin the study of data structures. There are many sorting algorithms that can sort elements in a given array or list. Counting sort is a sorting algorithm that has the best time complexity. However, the counting sort algorithm only works for positive integers. In this paper, an extension of the counting sort algorithm is proposed that can sort real numbers and integers (both positive and negative).

Complexity analysis and performance of double hashing sort algorithm

Sorting an array of n elements represents one of the leading problems in different fields of computer science such as databases, graphs, computational geometry, and bioinformatics. A large number of sorting algorithms have been proposed based on different strategies. Recently, a sequential algorithm, called double hashing sort (DHS) algorithm, has been shown to exceed the quick sort algorithm in performance by 10–25%. In this paper, we study this technique from the standpoints of complexity analysis and the algorithm’s practical performance. We propose a new complexity analysis for the DHS algorithm based on the relation between the size of the input and the domain of the input elements. Our results reveal that the previous complexity analysis was not accurate. We also show experimentally that the counting sort algorithm performs significantly better than the DHS algorithm. Our experimental studies are based on six benchmarks; the percentage of improvement was roughly 46% on the average for all cases studied.

Parallel Counting Sort: A Modified of Counting Sort Algorithm

Sorting is one of a classic problem in computer engineer. One well-known sorting algorithm is a Counting Sort algorithm. Counting Sort had one problem, it can’t sort a positive and negative number in the same input list. Then, Modified Counting Sort created to solve that’s problem. The algorithm will split the numbers before the sorting process begin. This paper will tell another modification of this algorithm. The algorithm called Parallel Counting Sort. Parallel Counting Sort able to increase the execution time about 70% from Modified Counting Sort, especially in a big dataset (around 1000 and 10.000 numbers).

Implementing and Analyzing an Efficient Version of Counting Sort (E-Counting Sort)

sort is a simple, stable and efficient sorting algorithm with linear running time, which is a fundamental building block for many applications. Counting sort algorithm has been widely used in data processing systems, because of its high efficiency, fast speed and stable nature. Therefore, a thorough study of its time complexity is required. This paper presents a modified version of counting sort E-Counting Sort with some efficiency improvements. An analysis of the E- Counting sort algorithm in comparison with original counting sort algorithm clearly shows that E-Counting sort algorithm having a reduced time complexity approximately to half of the original one. The new version can be applied to many real world applications providing the required result as efficient and as effective as original counting sort without affecting its real nature and improve the efficiency of application program.

Modified Pure Radix Sort for Large Heterogeneous Data Set

We have proposed a Modified Pure Radix Sort for Large Heterogeneous Data Set. In this research paper we discuss the problems of radix sort, brief study of previous works of radix sort & present new modified pure radix sort algorithm for large heterogeneous data set. We try to optimize all related problems of radix sort through this algorithm. This algorithm works on the Technology of Distributed Computing which is implemented on the principal of divide & conquer method. I. Introduction Sorting is a computational building block of fundamental importance and is the most widely studied algorithmic problem. The importance of sorting has led to the design of efficient sorting algorithms for a variety of architectures. Many applications rely on the availability of efficient sorting routines as a basis for their own efficiency, while some algorithms can be conveniently phrased in terms of sorting. Radix sort is an algorithm that sorts numbers by processing individual digits. n numbers consisting of k digits each are sorted in O (n · k) time. Radix sort can either process digits of each number starting from the least significant digit (LSD) or the most significant digit (MSD). The LSD algorithm first sorts the list by the least significant digit while preserving their relative order using a stable sort. Then it sorts them by the next digit, and so on from the least significant to the most significant, ending up with a sorted list. While the LSD radix sort requires the use of a stable sort, the MSD radix sort algorithm does not (unless stable sorting is desired). MSD radix sort is not stable. It is common for the counting sort algorithm to be used internally by the radix sort; Hybrid sorting approach, such as using insertion sort for small bins improves performance of radix sort significantly.

Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation

Attribute reduction is one of the key issues in rough set theory. Many heuristic attribute reduction algorithms such as positive-region reduction, information entropy reduction and discernibility matrix reduction have been proposed. However, these methods are usually computationally time-consuming for large data. Moreover, a single attribute significance measure is not good for more attributes with the same greatest value. To overcome these shortcomings, we first introduce a counting sort algorithm with time complexity O(∣ C∣ ∣ U∣) for dealing with redundant and inconsistent data in a decision table and computing positive regions and core attributes (∣ C∣ and ∣ U∣ denote the cardinalities of condition attributes and objects set, respectively). Then, hybrid attribute measures are constructed which reflect the significance of an attribute in positive regions and boundary regions. Finally, hybrid approaches to attribute reduction based on indiscernibility and discernibility relation are proposed with time complexity no more than max( O(∣ C∣ 2∣ U/ C∣), O(∣ C∣∣ U∣)), in which ∣ U/ C∣ denotes the cardinality of the equivalence classes set U/ C. The experimental results show that these proposed hybrid algorithms are effective and feasible for large data.