Year: 2020
Type: preprint
Abstract: In this paper we propose a second--order method for solving \emph{linear composite sparse optimization problems} consisting of minimizing the sum of a differentiable (possibly nonconvex function) and a nondifferentiable convex term. The composite nondifferentiable convex penalizer is given by $\ell_1$--norm of a matrix multiplied with the coefficient vector. The algorithm that we propose for the case of the linear composite $\ell_1$ problem relies on the three main ingredients that power the OESOM algorithm \cite{dlrlm07}: the minimum norm subgradient, a projection step and, in particular, the second--order information associated to the nondifferentiable term. By extending these devices, we obtain a full second--order method for solving composite sparse optimization problems which includes a wide range of applications. For instance, problems involving the minimization of a general class \emph{differential graph operators} can be solved with the proposed algorithm. We present several computational experiments to show the efficiency of our approach for different application examples.
Source: arXiv (Cornell University)
Authors: Merino, Pedro, Reyes, Juan Carlos De Los
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Escuela Politécnica Nacional
OpenAlex ID: W3133415787