Saved in:
Bibliographic Details
Main Authors: Luo, Zhenhua, Yuan, Gonglin, Pham, Hongtruong
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2409.05002
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866916827754397696
author Luo, Zhenhua
Yuan, Gonglin
Pham, Hongtruong
author_facet Luo, Zhenhua
Yuan, Gonglin
Pham, Hongtruong
contents We integrate the diagonal quasi-Newton update approach with the enhanced BFGS formula proposed by Wei, Z., Yu, G., Yuan, G., Lian, Z. \cite{b1}, incorporating extrapolation techniques and inertia acceleration technology. This method, designed specifically for non-convex constrained problems, requires that the search direction ensures sufficient descent and establishes global linear convergence. Such a design has yielded exceptionally favorable data results.
format Preprint
id arxiv_https___arxiv_org_abs_2409_05002
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Diagonal BFGS Update Algorithm with Inertia Acceleration Technology for Minimizations
Luo, Zhenhua
Yuan, Gonglin
Pham, Hongtruong
Optimization and Control
We integrate the diagonal quasi-Newton update approach with the enhanced BFGS formula proposed by Wei, Z., Yu, G., Yuan, G., Lian, Z. \cite{b1}, incorporating extrapolation techniques and inertia acceleration technology. This method, designed specifically for non-convex constrained problems, requires that the search direction ensures sufficient descent and establishes global linear convergence. Such a design has yielded exceptionally favorable data results.
title A Diagonal BFGS Update Algorithm with Inertia Acceleration Technology for Minimizations
topic Optimization and Control
url https://arxiv.org/abs/2409.05002