Abstract
In this paper, we follow a chronological development of gradient descent methods and its accelerated variants later on. We specifically emphasise some contemporary approaches within this research field. Accordingly, a constructive overview over the class of hybrid accelerated models derived from the three-term hybridization process proposed by Khan is presented. Extensive numerical test results illustrate the performance profiles of hybrid and non-hybrid versions of chosen accelerated gradient models regarding the number of iterations, CPU time, and number of function evaluation metrics. Favorable outcomes justify this hybrid approach as an accepted method in developing new efficient optimization schemes.
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