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^*

^*Corresponding author for this work

School of Physics

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	941-945
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	25
Issue number	7
DOIs	https://doi.org/10.1109/LSP.2018.2833428
Publication status	Published - Jul 2018

Keywords

Corrupted sensing
data-time tradeoffs
projected gradient descent (PGD)

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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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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.

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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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Keywords

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