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This repository provides a Matlab implementation of the ADMM-based solvers for L1-L1 and L1-L2 minimization.

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joaofcmota/cs-with-prior-information

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Compressed Sensing with Prior Information: Optimal Strategies, Geometry, and Bounds

Matlab solvers for - minimization:

- minimization:

and also for Modified-CS:

The first two problems are analyzed in

  1. Compressed Sensing with Prior Information: Strategies, Geometry, and Bounds.
    J. F. C. Mota, N. Deligiannis, M. R. D. Rodrigues.
    IEEE Transactions on Information Theory, Vol. 63, No. 7, pp. 4472-4496, 2017.
    link, arXiv

and

  1. Compressed Sensing with Side Information: Geometrical Interpretation and Performance Bounds.
    J. F. C. Mota, N. Deligiannis, M. Rodrigues.
    IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, 2014. Session: Information Processing for Big Data.
    link, arXiv

and Modified-CS was proposed in

  1. Modified-CS: Modifying Compressive Sensing for Problems With Partially Known Support.
    N. Vaswani, W. Lu.
    IEEE Transactions on Signal Processing, Vol. 58, No. 9, 2010.
    link

This page also contains code to reproduce the experiments and figures in [1].

Organization

  • solvers: code for - minimization, - minimization, Modified-CS. Each folder contains a detailed derivation of the respective algorithm.

  • createFigures: code for reproducing the figures in [1].


License: GPLv3

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This repository provides a Matlab implementation of the ADMM-based solvers for L1-L1 and L1-L2 minimization.

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