A hybrid quasi-Newton projected-gradient method with application to Lasso and basis-pursuit denoising

摘要

We propose a new algorithm for the optimization of convex functions over a polyhedral set in Rndocumentclass[12pt]{minimal}usepackage{amsmath}usepackage{wasysym}usepackage{amsfonts}usepackage{amssymb}usepackage{amsbsy}usepackage{mathrsfs}usepackage{upgreek}setlength{oddsidemargin}{-69pt}egin{document}$${mathbb {R}}^n$$end{document}. The algorithm extends the spectral projected-gradient method with limited-memory BFGS iterates restricted to the present face whenever possible. We prove convergence of the algorithm under suitable conditions and apply the algorithm to solve the Lasso problem, and consequently, the basis-pursuit denoise problem through the root-finding framework proposed by van den Berg and Friedlander (SIAM J Sci Comput 31(2):890–912, 2008). The algorithm is especially well suited to simple domains and could also be used to solve bound-constrained problems as well as problems restricted to the simplex.