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Modified multi-direction iterative algorithm for separable nonlinear models with missing data

Chen, Jing; Hu, Manfeng; Mao, Yawen; Zhu, Quanmin

Authors

Jing Chen

Manfeng Hu

Yawen Mao

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Quan Zhu Quan.Zhu@uwe.ac.uk
Professor in Control Systems



Abstract

Multi-direction iterative (MUL-DI) algorithm is an efficient algorithm for large-scale models, and it establishes a theoretical linkage between least squares (LS) and gradient descent (GD) algorithms. However, it involves Givens transformation and dense matrix calculation in each iteration, which leads to heavy computational efforts. In this letter, a modified MUL-DI algorithm is proposed for separable nonlinear models with missing data. Several directions are designed using a diagonal matrix, and their corresponding step-sizes are obtained based on LS algorithm. Compared with the traditional algorithms, the algorithm proposed in this letter has the following advantages: (1) has a faster convergence rate; (2) has a simple cost function; (3) is more robust to the condition number; (4) has less computational efforts. A simulation example shows the effectiveness of the modified MUL-DI algorithm.

Citation

Chen, J., Hu, M., Mao, Y., & Zhu, Q. (2022). Modified multi-direction iterative algorithm for separable nonlinear models with missing data. IEEE Signal Processing Letters, 29, 1968-1972. https://doi.org/10.1109/LSP.2022.3204408

Journal Article Type Article
Acceptance Date Aug 31, 2022
Online Publication Date Sep 5, 2022
Publication Date Sep 5, 2022
Deposit Date Nov 4, 2022
Publicly Available Date Sep 6, 2024
Journal IEEE Signal Processing Letters
Print ISSN 1070-9908
Electronic ISSN 1558-2361
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 29
Pages 1968-1972
DOI https://doi.org/10.1109/LSP.2022.3204408
Keywords Applied Mathematics, Electrical and Electronic Engineering, Signal Processing, Convergence rate, multi-direction iterative algorithm, missing data, separable nonlinear model
Public URL https://uwe-repository.worktribe.com/output/10024003
Publisher URL https://ieeexplore.ieee.org/document/9878024