A large number of imaging problems reduce to the optimization of a cost function, with
typical structural properties. The aim of this paper is to describe the state of the art in …

In recent years, unfolding iterative algorithms as neural networks has become an empirical
success in solving sparse recovery problems. However, its theoretical understanding is still …

In this article, we propose a deep extension of sparse subspace clustering, termed deep
subspace clustering with L1-norm (DSC-L1). Regularized by the unit sphere distribution …

We investigate implicit regularization schemes for gradient descent methods applied to
unpenalized least squares regression to solve the problem of reconstructing a sparse signal …

In this paper, we investigate a group sparse optimization problem via lp, q regularization in
three aspects: theory, algorithm and application. In the theoretical aspect, by introducing a …

In this paper, we consider a class of Forward--Backward (FB) splitting methods that includes
several variants (eg, inertial schemes, FISTA) for minimizing the sum of two proper convex …

In this paper, we study the proximal gradient algorithm with extrapolation for minimizing the
sum of a Lipschitz differentiable function and a proper closed convex function. Under the …

We propose generic acceleration schemes for a wide class of optimization and iterative
schemes based on relaxation and inertia. In particular, we introduce methods that …

We present and analyze a backtracking strategy for a general fast iterative shrinkage/
thresholding algorithm proposed by Chambolle and Pock [Acta Numer., 25 (2016), pp. 161 …

The sparse representation-based classification (SRC) has been utilized in many
applications and is an effective algorithm in machine learning. However, the performance of …