Data-Time Tradeoffs for Corrupted Sensing

Jinchi Chen; Yulong Liu

doi:10.1109/LSP.2018.2833428

Data-Time Tradeoffs for Corrupted Sensing

Jinchi Chen
, Yulong Liu^*

^*此作品的通讯作者

物理学院

Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

2 引用（Scopus）

摘要

In this letter, we characterize a data-time tradeoff for projected gradient descent (PGD) algorithms used for solving corrupted sensing problems under sub-Gaussian measurements. We also show that with a proper step size, the PGD method can achieve a linear rate of convergence when the number of measurements is sufficiently large.

源语言	英语
页（从-至）	941-945
页数	5
期刊	IEEE Signal Processing Letters
卷	25
期	7
DOI	https://doi.org/10.1109/LSP.2018.2833428
出版状态	已出版 - 7月 2018

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10.1109/LSP.2018.2833428

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title = "Data-Time Tradeoffs for Corrupted Sensing",

abstract = "In this letter, we characterize a data-time tradeoff for projected gradient descent (PGD) algorithms used for solving corrupted sensing problems under sub-Gaussian measurements. We also show that with a proper step size, the PGD method can achieve a linear rate of convergence when the number of measurements is sufficiently large.",

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author = "Jinchi Chen and Yulong Liu",

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AB - In this letter, we characterize a data-time tradeoff for projected gradient descent (PGD) algorithms used for solving corrupted sensing problems under sub-Gaussian measurements. We also show that with a proper step size, the PGD method can achieve a linear rate of convergence when the number of measurements is sufficiently large.

KW - Corrupted sensing

KW - data-time tradeoffs

KW - projected gradient descent (PGD)

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DO - 10.1109/LSP.2018.2833428

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JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

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Data-Time Tradeoffs for Corrupted Sensing

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