11-magic : Recovery of sparse signals via convex programming

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Compressed Sensing (2014, unknown): “Sin embargo, es posible reformularlo como un problema de programación lineal (LP), de la siguiente forma [16][Apéndice A]:”
Kernel Sparse Representation-Based Classifier (2012, methodology): “The quadratically constrained -minimization problem for both KSRC and SRC is solved by exploiting -MAGIC software package [38].”
Fixed point and Bregman iterative methods for matrix rank minimization (2009, methodology): “6) have been widely investigat ed nd many algorithms have been suggested including convex optimization methods ([2; 10; 20; 24; 27]) and heuris tic methods ([13; 15; 16; 39; 40]).”
A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm (2008, methodology): “Performance analysis In this experiment, we study the computational cost of the presented method, and compare its performance with `1magic [25] as one of the fastest implementations of interiorpoint LP, and with FOCUSS9.”
Ensemble Sparse Classification of Alzheimer’s Disease (2012, unknown): “The L1-norm minimization in Equation (1) can be efficiently solved by using some L1regularized sparse coding methods such as those proposed in (Boyd and Vandenberghe, 2004; Candes and Romberg, 2005; Chen et al., 2001; Kim et al., 2007).”