COMPARISON OF REFINEMENT ACCELERATED RELAXATION ITERATIVE TECHNIQUES AND CONJUGATE GRADIENT TECHNIQUE FOR LINEAR SYSTEMS

Abstract

Iterative methods use consecutive approximations to get more accurate results. A comparison of three iterative approaches to solving linear systems of this type My=B is provided in this paper. We surveyed the Refinement Accelerated Relaxation technique, Refinement Extended Accelerated Relaxation technique, and Conjugate Gradient technique, and demonstrated algorithms for each of these approaches in order to get to the solutions more quickly. The algorithms are then transformed into the Python language and used as iterative methods to solve these linear systems. Some numerical investigations were carried out to examine and compare their convergence speeds. Based on performance metrics such as convergence time, number of iterations required to converge, storage, and accuracy, the research demonstrates that the conjugate gradient method is superior to other approaches, and it is important to highlight that the conjugate gradient technique is not stationary. These methods can help in situations that are similar to finite differences, finite element methods for solving partial differential equations, circuit and structural analysis. Based on the results of this study, iteration techniques will be used to help analysts understand systems of linear algebraic equations.