@article {10.3844/jcssp.2020.1610.1624,
article_type = {journal},
title = {The New Matrix Model of Computation Based Purely on Quite a New Concept of the Matrix Computations for Extremely Quick Web Pages Loading},
author = {Chotchaeva, Zulfia A.},
volume = {16},
number = {11},
year = {2020},
month = {Nov},
pages = {1610-1624},
doi = {10.3844/jcssp.2020.1610.1624},
url = {https://thescipub.com/abstract/jcssp.2020.1610.1624},
abstract = {We all dream of quick loading. Quicker than it is now. So that it loads immediately. What is needed for this? There are a lot of things to do. The most important things in it are computations. To speed up loading, we need to speed up computations. If we will find the way to multiply large numbers quicker than we have, the loading will be much quicker. How to do that? We need to multiply large numbers in time O(log n). How is that possible? A new model of computation may solve this problem. These are the algorithms that require considerably less amount of resources to perform them. The time complexity of the algorithm is the main (key) resource that we need to reduce to get the desired complexity. It seems incredible, but it is possible. We will get this through the sorting array. Best, worst, and average cases of a given algorithm could be considered for each particular input instance of the problem when analyzing algorithms. The worst-case complexity is the most used in algorithm analysis, it gives an upper bound on the resources required by the algorithm. Thus, the discovery of better algorithms brings the upper bound on the worst-case running time down. This paper presents the new matrix model of computation, which is based on the concept of the new matrix computations for advanced computing. The paper intends to prove the existence of better algorithms for any given input instance of the worst-case time complexity M(n) = O(n2) that take O(log n) and provide extremely quick web pages loading and create a new topic in complexity.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}