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Bibliographic Details
Main Authors: Dimitrieski, Naum, Reyer, Michael, Belabbas, Mohamed-Ali, Ebenbauer, Christian
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2410.20572
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Table of Contents:
  • In this paper a novel stochastic optimization and extremum seeking algorithm is presented, one which is based on time-delayed random perturbations and step size adaptation. For the case of a one-dimensional quadratic unconstrained optimization problem, global exponential convergence in expectation and global exponential practical convergence of the variance of the trajectories are proven. The theoretical results are complemented by numerical simulations for one- and multi-dimensional quadratic and non-quadratic objective functions.