AI-POWERED SELF-DECISIVE ALGORITHM FOR TWO-STEP QUASI-NEWTON METHODS

Abstract

The rapid evolution of machine learning has introduced a wide range of challenging and significant optimization problems. Various algorithms have been developed and trained to obtain optimal solutions for diverse problems in science, engineering, medicine, and related fields through machine learning techniques. In this context, fast gradient-driven optimization algorithms have become essential for computationally efficient model training. This study investigates an AI-powered self-decisive algorithm based on image-processing techniques for solving nonlinear unconstrained optimization problems. Different skipping strategies and search-direction modification techniques are incorporated within the framework of two-step quasi-Newton methods. Two test functions with different dimensions and initial points are examined using the fixed-point approach. The numerical simulations support the selection of the most suitable strategy for the proposed self-decisive algorithm in two-step quasi-Newton methods.