A Novel Prescribed-Time Convergence Acceleration Algorithm with Time Rescaling

Xuehui Mei (), Pengrui Zhang, Haijun Jiang and Zhiyong Yu
Additional contact information
Xuehui Mei: College of Mathematics and System Science, Xinjiang University, Urumqi 830047, China
Pengrui Zhang: College of Mathematics and System Science, Xinjiang University, Urumqi 830047, China
Haijun Jiang: School of Mathematics and Statistics, YiLi Normal University, Yining 835000, China
Zhiyong Yu: College of Mathematics and System Science, Xinjiang University, Urumqi 830047, China

Mathematics, 2025, vol. 13, issue 2, 1-28

Abstract: In machine learning, the processing of datasets is an unavoidable topic. One important approach to solving this problem is to design some corresponding algorithms so that they can eventually converge to the optimal solution of the optimization problem. Most existing acceleration algorithms exhibit asymptotic convergence. In order to ensure that the optimization problem converges to the optimal solution within the prescribed time, a novel prescribed-time convergence acceleration algorithm with time rescaling is presented in this paper. Two prescribed-time acceleration algorithms are constructed by introducing time rescaling, and the acceleration algorithms are used to solve unconstrained optimization problems and optimization problems containing equation constraints. Some important theorems are given, and the convergence of the acceleration algorithms is proven using the Lyapunov function method. Finally, we provide numerical simulations to verify the effectiveness and rationality of our theoretical results.

Keywords: exponential prescribed-time convergence; acceleration algorithm; time rescaling; optimization problem; Lyapunov function (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/2/251/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/2/251/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:2:p:251-:d:1566191

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().