Comparative performance analysis of some accelerated and hybrid accelerated gradient models

Abstract

We analyze a performance profile of several accelerated and hybrid accelerated methods. All comparative methods are at least linearly convergent and have satisfied numerical characteristics regarding tested metrics: number of iterations, CPU time and number of function evaluations. Among the chosen set of methods we numerically show which one is the most efficient and the most effective. Therewith, we derived a conclusion about what type of method is more preferable to use considering analyzed metrics.

Keywords

• Gradient descent methods
• Line search
• Convergence rate