The Steepest descent method and the Conjugate gradient to minimize nonlinear functions have been studied in this work. Algorithms are presented implemented Matlab software for both methods. However, a comparison has made between method. obtained results time efficiency aspects. It is shown that needs fewer iterations more than On other hand, converges function less

The q-gradient method used a Yuan step size for odd steps, and geometric recursion as an even (q-GY). This study aimed to accelerate convergence minimum point by minimizing the number of iterations, dilating parameter q independent variable then comparing results with three algorithms namely, classical steepest descent (SD) method, Steps (SDY), (q-G). numerical were presented in tables graphs. ...