"A descent three-term derivative-free method for signal reconstruction in compressive sensing"

IF 1.4 4区 数学 Q1 MATHEMATICS
A. Ibrahim, P. Kumam, A. Abubakar, Jamilu Abubakar
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引用次数: 0

Abstract

"Many real-world phenomena in engineering, economics, statistical inference, compressed sensing and machine learning involve finding sparse solutions to under-determined or ill-conditioned equations. Our interest in this paper is to introduce a derivative-free method for recovering sparse signal and blurred image arising in compressed sensing by solving a nonlinear equation involving a monotone operator. The global convergence of the proposed method is established under the assumptions of monotonicity and Lipschitz continuity of the underlying operator. Numerical experiments are performed to illustrate the efficiency of the proposed method in the reconstruction of sparse signals and blurred images."
压缩感知中信号重构的一种下降三项无导数方法
“工程、经济学、统计推断、压缩感知和机器学习中的许多现实世界现象都涉及到为欠确定或病态方程寻找稀疏解。在本文中,我们的兴趣是通过求解涉及单调算子的非线性方程来引入一种无导数的方法来恢复压缩感知中产生的稀疏信号和模糊图像。在基础算子单调性和Lipschitz连续性的假设下,证明了该方法的全局收敛性。数值实验验证了该方法在稀疏信号和模糊图像重建中的有效性。
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来源期刊
Carpathian Journal of Mathematics
Carpathian Journal of Mathematics MATHEMATICS, APPLIED-MATHEMATICS
CiteScore
2.40
自引率
7.10%
发文量
21
审稿时长
>12 weeks
期刊介绍: Carpathian Journal of Mathematics publishes high quality original research papers and survey articles in all areas of pure and applied mathematics. It will also occasionally publish, as special issues, proceedings of international conferences, generally (co)-organized by the Department of Mathematics and Computer Science, North University Center at Baia Mare. There is no fee for the published papers but the journal offers an Open Access Option to interested contributors.
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