A lagrange programming neural network approach for nuclear norm optimization

Xiangguang Dai; Jian Qiu; Chaoyang Wan; Facheng Dai

doi:10.1371/journal.pone.0292380

A lagrange programming neural network approach for nuclear norm optimization

PLoS One. 2024 Feb 8;19(2):e0292380. doi: 10.1371/journal.pone.0292380. eCollection 2024.

Authors

Xiangguang Dai¹, Jian Qiu², Chaoyang Wan², Facheng Dai³

Affiliations

¹ Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology, Chongqing Three Gorges University, Chongqing, China.
² Chongqing Three Gorges University, Chongqing, China.
³ Chongqing University of Posts and Telecommunications, Chongqing, China.

Abstract

This article proposes a continuous-time optimization approch instead of tranditional optimiztion methods to address the nuclear norm minimization (NNM) problem. Refomulating the NNM into a matrix form, we propose a Lagrangian programming neural network (LPNN) to solve the NNM. Moreover, the convergence condtions of LPNN are presented by the Lyapunov method. Convergence experiments are presented to demonstrate the convergence of LPNN. Compared with tranditional algorithms of NNM, the proposed algorithm outperforms in terms of image recovery.

Copyright: © 2024 Dai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

MeSH terms

Algorithms*
Neural Networks, Computer*

Grants and funding

The work described in this paper was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-M202201204, KJZD-K202201205), the Opening fund of Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology (Grant No. zhlv-20221007, zhlv-20221002), Science and Technology Innovation Smart Agriculture Project of Science and Technology Department, Wanzhou District of Chongqing (Grant No. 2022-17), and the Opening Project of Sichuan Province University Key Laboratory of Bridge Non-destruction Detecting and Engineering Computing (Grant No. 2022QYY04). All grants were received by XD.