# Non-convex compressed sensing using partial support information

 Title Non-convex compressed sensing using partial support information Publication Type SINBAD Presentation Authors Navid Ghadermarzy Publisher SINBAD Year of Publication 2012 Abstract In this talk, we will address the recovery conditions of weighted $\ell_p$ minimization for signal reconstruction from compressed sensing measurements when (possibly inaccurate) partial support information is available. First we will motivate the use of (weighted) $\ell_p$ minimization with $p<1$ and point out its advantages over weighted $\ell_1$ minimization when there is prior information on the support of the signal that is possibly partial and inaccurate. Then we will provide theoretical guarantees of sufficient recovery conditions for weighted $\ell_p$ minimization, which are better than those for (unweighted) $\ell_p$ minimization as well as those for weighted $\ell_1$. In the last part of the talk, we will illustrate our results with some numerical experiments stylized applications. Keywords Presentation, SINBAD, SINBADFALL2012, SLIM URL https://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2012/Fall/ghadermarzy2012SINBADncc/ghadermarzy2012SINBADncc_pres.pdf Citation Key ghadermarzy2012SINBADncc