Result: A modified Levenberg–Marquardt algorithm for low order-value optimization problem: A modified Levenberg-Marquardt algorithm for low order-value optimization problem

Title:
A modified Levenberg–Marquardt algorithm for low order-value optimization problem: A modified Levenberg-Marquardt algorithm for low order-value optimization problem
Authors:
Xiaochen Lv, Zhensheng Yu
Source:
Journal of Applied Mathematics and Computing. 70:5629-5644
Publisher Information:
Springer Science and Business Media LLC, 2024.
Publication Year:
2024
Subject Terms:
global convergence, Numerical mathematical programming methods, Nonlinear programming, low order-value optimization, 0211 other engineering and technologies, 02 engineering and technology, Levenberg-Marquardt algorithm, outlier detection, 0101 mathematics, 01 natural sciences
Document Type:
Academic journal Article
File Description:
application/xml
Language:
English
ISSN:
1865-2085
1598-5865
DOI:
10.1007/s12190-024-02140-1
Access URL:
https://zbmath.org/8036676
https://doi.org/10.1007/s12190-024-02140-1
Rights:
CC BY
Accession Number:
edsair.doi.dedup.....9e7c7c224b122aab9f1bdbb48c5b155f
Database:
OpenAIRE

Further Information

In this paper, we consider a modified Levenberg–Marquardt algorithm for Low Order Value Optimization problems(LOVO). In the algorithm, we obtain the search direction by a combination of LM steps and approximate LM steps, and solve the subproblems therein by QR decomposition or cholesky decomposition. We prove the global convergence of the algorithm theoretically and discuss the worst-case complexity of the algorithm. Numerical results show that the algorithm in this paper is superior in terms of number of iterations and computation time compared to both LM-LOVO and GN-LOVO algorithm.