A number of our pages use cookies to identify you when you sign-in to our site.
The cookie-related information is fully under our control. These cookies are not used for any purpose other than those described here. Unibo policy
By continuing to browse the site, or by clicking on “close”, you consent to the use of cookies.
Inspired by techniques used for alternating optimization of nonconvex functions, we propose a simple yet effective algorithm with better trade-off between accuracy and computation time than the state-of-the-art for the nonconvex $\ell_0$ regularized optimization ($\ell_0$-RO) problem. Given an initial solution, we first find the vanilla solution to $\ell_0$-RO via a descent method (Nesterov’s AGP), to then estimate a new one by scaling the dictionary involved in $\ell_0$-RO, considering only a reduced number of its atoms.
This presentation is part of Contributed Presentation “CP1 - Contributed session 1”