Recently, alternating direction method of multipliers (ADMM) attracts much attentions from
various fields and there are many variant versions tailored for different models. Moreover, its …

Abstract The Extreme Learning Machine is a single-hidden-layer feedforward learning
algorithm, which has been successfully applied in regression and classification problems in …

This paper gives an overview of the majorization-minimization (MM) algorithmic framework,
which can provide guidance in deriving problem-driven algorithms with low computational …

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 this paper, we propose an inertial forward-backward splitting algorithm to compute a zero
of the sum of two monotone operators, with one of the two operators being co-coercive. The …

In this paper we study an algorithm for solving a minimization problem composed of a
differentiable (possibly nonconvex) and a convex (possibly nondifferentiable) function. The …

We study the convergence of general descent methods applied to a lower semi-continuous
and nonconvex function, which satisfies the Kurdyka–Łojasiewicz inequality in a Hilbert …

Natural image statistics indicate that we should use nonconvex norms for most
regularization tasks in image processing and computer vision. Still, they are rarely used in …

A number of recent works have emphasized the prominent role played by the Kurdyka-
Łojasiewicz inequality for proving the convergence of iterative algorithms solving possibly …

We propose a forward–backward proximal-type algorithm with inertial/memory effects for
minimizing the sum of a nonsmooth function with a smooth one in the nonconvex setting …