Hybrid Modified Accelerated Gradient Method for Optimization Processes

Abstract

This research reveals a hybrid variant of the modified accelerated gradient method. We prove that derived iteration is linearly convergent on the set of uniformly convex functions. Performance profiles of the introduced hybrid method were numerically compared with its non-hybrid version. The analyzed characteristics were CPU time, the number of iterations and the number of function evaluations. The results of the numerical experiments show a better performance in favor of the derived hybrid accelerated model compared with its forerunner.