APPROACHES TO PRACTICAL ANALYSIS OF COMPUTING ALGORITHMS

Abstract

The present work proposes a practical approach to determining the main types of algorithms, depending on their effectiveness in the appearance of the software code. Examples of analysis of the software code for computational complexity are given in the order of reducing the efficiency supplied as (in asymptotic designations): O(1), O(LogN), O(N), O(NlogN), O(N2), O(N2), O(N2), O(N3). The research task was to analyze the software code and specific conditions in which the algorithm refers to a particular type of computational complexity. The aim of analyzing the complexity of algorithms is to find the optimal algorithm for solving a specific problem. The criterion of optimality of the algorithm is chosen by the complexity of the algorithm, i.e., the number of elementary operations that must be performed to solve the problem using this algorithm. The complexity function is the ratio that connects the algorithm’s input data with the number of elementary operations. The paper contains a description of classical computational complexity that can be revealed by visual analysis of program code. The main types of computational complexity are (listed in descending order of efficiency) constant, logarithmic, linear, linear-logarithmic, quadratic, cubic. Also, methods for the determination of computational complexity are described. It is established that the main factors that can assess the algorithm’s computational complexity for the visual analysis of the software code are the presence of cycles, especially enclosed, reversibility of the algorithm, etc. Further research could usefully explore a method of semantic analysis of program code to predict the assessment of its computational complexity.