On a gradient-based algorithm for sparse signal reconstruction in the signal/measurements domain

DOI10.1155/2016/6212674zbMath1400.94064OpenAlexW2441544549WikidataQ59140853 ScholiaQ59140853MaRDI QIDQ1793419

Miloš Daković, Ljubisa Stanković

Publication date: 12 October 2018

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2016/6212674

Mathematics Subject Classification ID

Quadratic programming (90C20) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (1)

Reconstruction of sparse signals in impulsive disturbance environments

Uses Software

• CONV_QP
• GPDT
• CoSaMP

Cites Work

• The restricted isometry property and its implications for compressed sensing
• CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
• Implementing proximal point methods for linear programming
• On the uniqueness of the sparse signals reconstruction based on the missing samples variation analysis
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• On the Solution of Large Quadratic Programming Problems with Bound Constraints
• Time-Frequency Energy Distributions Meet Compressed Sensing
• Signal Decomposition by Using the S-Method With Application to the Analysis of HF Radar Signals in Sea-Clutter
• Regression Shrinkage and Selection via The Lasso: A Retrospective
• An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
• Matching pursuits with time-frequency dictionaries
• Gradient projection methods for quadratic programs and applications in training support vector machines
• Compressed sensing
• A measure of some time-frequency distributions concentration.
• Adaptive greedy approximations

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