Neurodynamics-based Iteratively Reweighted Convex Optimization for Sparse Signal Reconstruction

Hangjun Che; Jun Wang; Andrzej Cichocki

doi:10.1109/ICIST55546.2022.9926780

Neurodynamics-based Iteratively Reweighted Convex Optimization for Sparse Signal Reconstruction

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

In this paper, sparse signal reconstruction is for-mulated a q-ratio minimization problem subjecting to linear underdetermined equations. In view of the nonconvexity of the objective function, the q-ratio formulation with q = 2 is approximately reformulated as an iteratively reweighted convex optimization problem in the majorization-minimization frame-work. A neurodynamic optimization approach is introduced to solve the formulated problem iteratively. The experimental results on sparse signal reconstruction are discussed to demonstrate the performance of the proposed approach.

Original language	English
Title of host publication	2022 12th International Conference on Information Science and Technology (ICIST)
Publisher	IEEE
Pages	45-51
ISBN (Electronic)	9781665485821
ISBN (Print)	978-1-6654-9738-1
DOIs	https://doi.org/10.1109/ICIST55546.2022.9926780
Publication status	Published - 2022
Event	12th International Conference on Information Science and Technology (ICIST 2022) - Zhongzhou Huayue International Hotel (开封中州华悦国际饭店), Kaifeng, Henan, China Duration: 14 Oct 2022 → 16 Oct 2022 https://conference.cs.cityu.edu.hk/icist/

Publication series

Name	International Conference on Information Science and Technology, ICIST
ISSN (Print)	2164-4357
ISSN (Electronic)	2573-3311

Conference

Conference	12th International Conference on Information Science and Technology (ICIST 2022)
Place	China
City	Kaifeng, Henan
Period	14/10/22 → 16/10/22
Internet address	https://conference.cs.cityu.edu.hk/icist/

Funding

This work is supported in part by the Fundamental Research Funds for the Central Universities (Grant No.SWU020006),by the National Natural Science Foundation of China (Grant # 62003281), by Natural Science Foundation of Chongqing (Grant cstc2021jcyj-msxmX1169), by the Research Grants Council of the Hong Kong Special Administrative Region of China (Grants 11202318 and 11202019), and by the Ministry of Science and Higher Education of the Russian Federation (Grant 075-10-2021-068).

Research Keywords

iteratively reweighted convex optimization
neurodynamic op-timization
q-ratio minimization
Sparse signal reconstruction

RGC Funding Information

RGC-funded

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10.1109/ICIST55546.2022.9926780

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Cite this

@inproceedings{411fc7d7bb964e768088139e2ed68195,

title = "Neurodynamics-based Iteratively Reweighted Convex Optimization for Sparse Signal Reconstruction",

abstract = "In this paper, sparse signal reconstruction is for-mulated a q-ratio minimization problem subjecting to linear underdetermined equations. In view of the nonconvexity of the objective function, the q-ratio formulation with q = 2 is approximately reformulated as an iteratively reweighted convex optimization problem in the majorization-minimization frame-work. A neurodynamic optimization approach is introduced to solve the formulated problem iteratively. The experimental results on sparse signal reconstruction are discussed to demonstrate the performance of the proposed approach.",

keywords = "iteratively reweighted convex optimization, neurodynamic op-timization, q-ratio minimization, Sparse signal reconstruction",

author = "Hangjun Che and Jun Wang and Andrzej Cichocki",

year = "2022",

doi = "10.1109/ICIST55546.2022.9926780",

language = "English",

isbn = "978-1-6654-9738-1",

series = "International Conference on Information Science and Technology, ICIST",

publisher = "IEEE",

pages = "45--51",

booktitle = "2022 12th International Conference on Information Science and Technology (ICIST)",

address = "United States",

note = "12th International Conference on Information Science and Technology (ICIST 2022) ; Conference date: 14-10-2022 Through 16-10-2022",

url = "https://conference.cs.cityu.edu.hk/icist/",

}

Che, H , Wang, J & Cichocki, A 2022, Neurodynamics-based Iteratively Reweighted Convex Optimization for Sparse Signal Reconstruction. in 2022 12th International Conference on Information Science and Technology (ICIST). International Conference on Information Science and Technology, ICIST, IEEE, pp. 45-51, 12th International Conference on Information Science and Technology (ICIST 2022), Kaifeng, Henan, China, 14/10/22. https://doi.org/10.1109/ICIST55546.2022.9926780

Neurodynamics-based Iteratively Reweighted Convex Optimization for Sparse Signal Reconstruction. / Che, Hangjun ; Wang, Jun; Cichocki, Andrzej.
2022 12th International Conference on Information Science and Technology (ICIST). IEEE, 2022. p. 45-51 (International Conference on Information Science and Technology, ICIST).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Neurodynamics-based Iteratively Reweighted Convex Optimization for Sparse Signal Reconstruction

AU - Che, Hangjun

AU - Wang, Jun

AU - Cichocki, Andrzej

PY - 2022

Y1 - 2022

N2 - In this paper, sparse signal reconstruction is for-mulated a q-ratio minimization problem subjecting to linear underdetermined equations. In view of the nonconvexity of the objective function, the q-ratio formulation with q = 2 is approximately reformulated as an iteratively reweighted convex optimization problem in the majorization-minimization frame-work. A neurodynamic optimization approach is introduced to solve the formulated problem iteratively. The experimental results on sparse signal reconstruction are discussed to demonstrate the performance of the proposed approach.

AB - In this paper, sparse signal reconstruction is for-mulated a q-ratio minimization problem subjecting to linear underdetermined equations. In view of the nonconvexity of the objective function, the q-ratio formulation with q = 2 is approximately reformulated as an iteratively reweighted convex optimization problem in the majorization-minimization frame-work. A neurodynamic optimization approach is introduced to solve the formulated problem iteratively. The experimental results on sparse signal reconstruction are discussed to demonstrate the performance of the proposed approach.

KW - iteratively reweighted convex optimization

KW - neurodynamic op-timization

KW - q-ratio minimization

KW - Sparse signal reconstruction

UR - http://www.scopus.com/inward/record.url?scp=85142612509&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85142612509&origin=recordpage

U2 - 10.1109/ICIST55546.2022.9926780

DO - 10.1109/ICIST55546.2022.9926780

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-6654-9738-1

T3 - International Conference on Information Science and Technology, ICIST

SP - 45

EP - 51

BT - 2022 12th International Conference on Information Science and Technology (ICIST)

PB - IEEE

T2 - 12th International Conference on Information Science and Technology (ICIST 2022)

Y2 - 14 October 2022 through 16 October 2022

ER -

Neurodynamics-based Iteratively Reweighted Convex Optimization for Sparse Signal Reconstruction

Abstract

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Research Keywords

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GRF: Collaborative Neurodynamic Approaches to Portfolio Optimization

GRF: Intelligent Mission Planning and Tracking Control of Autonomous Surface Vehicles Based on Neural Computation

Cite this

Neurodynamics-based Iteratively Reweighted Convex Optimization for Sparse Signal Reconstruction

Abstract

Publication series

Conference

Funding

Research Keywords

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

GRF: Collaborative Neurodynamic Approaches to Portfolio Optimization

GRF: Intelligent Mission Planning and Tracking Control of Autonomous Surface Vehicles Based on Neural Computation

Cite this