title: Rate of Convergence of the FOCUSS Algorithm.
abstract: Focal underdetermined system solver (FOCUSS) is a powerful method for basis selection and sparse representation, where it employs the [Formula: see text]-norm with p ∈ (0,2) to measure the sparsity of solutions. In this paper, we give a systematical analysis on the rate of convergence of the FOCUSS algorithm with respect to p ∈ (0,2) . We prove that the FOCUSS algorithm converges superlinearly for and linearly for usually, but may superlinearly in some very special scenarios. In addition, we verify its rates of convergence with respect to p by numerical experiments.
authors.forename: Kan
authors.initials: K
authors.lastname: Xie
authors.forename: Zhaoshui
authors.initials: Z
authors.lastname: He
authors.forename: Andrzej
authors.initials: A
authors.lastname: Cichocki
authors.forename: Xiaozhao
authors.initials: X
authors.lastname: Fang