Recovering sparse signals from linear measurements has demonstrated outstanding utility
in a vast variety of real-world applications. Compressive sensing is the topic that studies the …

We use a model LASSO problem to analyze the convergence behavior of the ISTA and
FISTA iterations, showing that both iterations satisfy local linear convergence rate bound …

Seeking sparse solutions of underdetermined linear systems is required in many areas of
engineering and science such as signal and image processing. The efficient sparse …

Consider the problem of reconstructing a multidimensional signal from an underdetermined
set of measurements, as in the setting of compressed sensing. Without any additional …

A new state estimation method is proposed for power distribution networks that suffer from
low-observability. The proposed distribution system state estimation (DSSE) method …

The major limitation of the Lagrange programming neural network (LPNN) approach is that
the objective function and the constraints should be twice differentiable. Since sparse …

This paper provides a variational analysis of the unconstrained formulation of the LASSO
problem, which is ubiquitous in statistical learning, signal processing, and inverse problems …

We provide a necessary and sufficient condition for the uniqueness of penalized least-
squares estimators whose penalty term is given by a norm with a polytope unit ball, covering …

The ℓ 1-synthesis model and the ℓ 1-analysis model recover structured signals from their
undersampled measurements. The solution of the former is a sparse sum of dictionary …

We study the super-resolution (SR) problem of recovering point sources consisting of a
collection of isolated and suitably separated spikes from only the low frequency …