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Matrix recovery using split bregman

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Matrix recovery using split bregman

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Recovery of low rank matrix, using Split Bregman, via nuclear norm minimization

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This work deals with recovering a low rank matrix from its lower dimensional projections via nuclear norm minimization.
% Minimize ||X||* (nuclear norm of Z)
% Subject to A(X) = Y
We use split Bregman algorithm for the same.
% Minimize (lambda1)||W||* + 1/2 || A(X) - y ||_2^2 + eta/2 || W-X-B1 ||_2^2
%W is proxy variable and B1 is the Bregman variable
The use of Bregman technique improves the convergence speeds of our algorithm and gives a higher success rate. Also, the accuracy of reconstruction is much better even for cases where small number of linear measurements are available.

Required Products MATLAB
MATLAB release MATLAB 8.1 (R2013a)
Other requirements Sparco toolbox http://www.cs.ubc.ca/labs/scl/sparco/ PROPACK http://soi.stanford.edu/~rmunk/PROPACK/
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