A new conjugate gradient for unconstrained optimization problems and its applications in neural networks

Alaa Luqman Ibrahim (Faculty of Science, University of Zakho)
Mohammed Guhdar Mohammed (Faculty of Science, University of Zakho)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 33, No 1: January 2024

We introduce a novel efficient and effective conjugate gradient approach for large-scale unconstrained optimization problems. The primary goal is to improve the conjugate gradient method's search direction in order to propose a new, more active method based on the modified vector , which is dependent on the step size of Barzilai and Borwein. The suggested algorithm features the following traits: (i) The ability to achieve global convergence; (ii) numerical results for large-scale functions show that the proposed algorithm is superior to other comparable optimization methods according to the number of iterations (NI) and the number of functions evaluated (NF); and (iii) training neural networks is done to improve their performance.