Quicker Sort Algorithm: Upgrading time complexity of Quick Sort to Linear Logarithmic

Budhani, Sandeep Kumar; Tewari, Naveen; Joshi, Mukesh; Kala, Kshitij

doi:10.1109/iccakm50778.2021.9357725

Cited by 2 publications

(1 citation statement)

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“…In the proposed technique benefit of the MMC control approach has been used and it is much quicker than quicksort. The authors [30] proposed a new algorithm based on the quicksort algorithm. The proposed algorithm gives better results for small as well as large datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Preprocessing: A method For Reducing Time Complexity

Mubarak

Iqbal

Rasool

et al. 2022

JCBI

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Data can be processed quickly if it is in some order, whereas unsequenced data can take more time to obtain results. Sorting is used for data arrangement. It is also one of the essential requirement for most applications and this step helps to boost performance. Sorting is also a prerequisite in several computer applications like databases. Over time computer scientists have not only introduced new sorting techniques considering various factors to be improved but they have also presented enhanced variants of existing sorting methods. The main objective has always been to reduce the execution time and space of the sorting algorithms. With every passing day, digital content is growing rapidly, which is a significant cause that encourages researchers to design new time-space efficient sorting algorithms. This paper presents some preprocessing strategies for quicksort and insertion sort to improve their performances. Tha main idea of using these preprocessings is to make input data more suitable for sorting algorithm, as most sorting function performs extraordinary for a specific type of input, such as insertion sort works better on nearyly sorted data. To authenticate the efficiency of existing sorting algorithms, these have been compared with proposed preprocessing strategies. The results with proposed techniqes outperforms the results of original sorting methods. It also helps to convert worst case into average case. By using this approch complexity of many algorithms can be reduced, therfore this is very important.

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Section: Literature Reviewmentioning

confidence: 99%