A New Non Quadratic Algorithm for Solving Non-Linear Optimization Problems

Abstract

This paper proposes a new algorithm for non-linear optimization to modify and develop the conjugate gradient (CG) methods and to obtain a strong global convergence. This algorithm is derived and evaluated numerically against the standard (P/R and H/S)-CG algorithms and T/S algorithm using more than (20) standard well-known test functions. The numerical results show that, Non-quadratic models are very beneficial in most of the problems especially when the dimensionality of the problem increases.