We propose an embedded/integrated feature selection method based on neural networks
with Group Lasso penalty. Group Lasso regularization is considered to produce sparsity on …

We consider a class of smoothing methods for minimization problems where the feasible set
is convex but the objective function is not convex, not differentiable and perhaps not even …

Recently, variable selection and sparse reconstruction are solved by finding an optimal
solution of a minimization model, where the objective function is the sum of a data-fitting …

In this paper, we study a general optimization model, which covers a large class of existing
models for many applications in imaging sciences. To solve the resulting possibly …

In this paper, based on the hybrid conjugate gradient method and the convex combination
technique, a new family of hybrid three-term conjugate gradient methods are proposed for …

Nonlinear conjugate gradient method (CGM) is one of the most efficient iterative methods for
dealing with large-scale optimization problems. In this paper, based on the Fletcher–Reeves …

Nonsmooth nonconvex regularization has remarkable advantages for the restoration of
piecewise constant images. Constrained optimization can improve the image restoration …

We propose a first order interior point algorithm for a class of non-Lipschitz and nonconvex
minimization problems with box constraints, which arise from applications in variable …

A nonconvex variational model is introduced which contains the \ell_q-``norm,” q∈(0,1), of
the gradient of the underlying image in the regularization part together with a least squares …

Image segmentation is of great importance in image processing. In this paper, we propose a
two-stage image segmentation strategy based on the nonconvex ℓ 2− ℓ p approximation of …