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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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author Dimitrieski, Naum
Reyer, Michael
Belabbas, Mohamed-Ali
Ebenbauer, Christian
author_facet Dimitrieski, Naum
Reyer, Michael
Belabbas, Mohamed-Ali
Ebenbauer, Christian
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.
format Preprint
id arxiv_https___arxiv_org_abs_2410_20572
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Time-delay Induced Stochastic Optimization and Extremum Seeking
Dimitrieski, Naum
Reyer, Michael
Belabbas, Mohamed-Ali
Ebenbauer, Christian
Optimization and Control
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.
title Time-delay Induced Stochastic Optimization and Extremum Seeking
topic Optimization and Control
url https://arxiv.org/abs/2410.20572