Surveying and comparing simultaneous sparse approximation (or group lasso) algorithms

We use the Multifrontal Massively Parallel Solver (MUMPS) to compute the solution of the system of equations. We also need to compute the variance of the solution, which amounts

In particular, in each iteration, we first construct a (possibly nonconvex) upper bound of the original objective function h(x) by the MM method, and then minimize an

In this work, we study the links between the recovery proper- ties of sparse signals for Orthogonal Matching Pursuit (OMP) and the whole General MP class over nested supports.. We

For this, we are going to call a modified version Algorithm BlockParametrization, where we update the values of our matrix sequences before computing the minimal matrix generator,

Then a low-rank matrix constraint which models the clutter is applied in order to decompose the useful signal into sparse coefficients in a blind source separation framework..

We consider the problem of non-parametric density estimation of a random environment from the observation of a single trajectory of a random walk in this environ- ment.. We

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