Comparative Analysis of Accelerated Models for Solving Unconstrained Optimization Problems with Application of Khan’s Hybrid Rule

Vladimir Rakočević and Milena J. Petrović ()
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Vladimir Rakočević: Serbian Academy of Sciences and Arts, Kneza Mihajla 35, 11000 Belgrade, Serbia
Milena J. Petrović: Faculty of Sciences and Mathematics, University of Pristina in Kosovska Mitrovica, Lole Ribara 29, 38220 Kosovska Mitrovica, Serbia

Mathematics, 2022, vol. 10, issue 23, 1-13

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.

Keywords: line search; gradient descent methods; quasi-Newton method; convergence rate (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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