In this paper, we study a split equilibrium problem and a fixed point problem with an infinite
family of strict pseudo-contractive mappings. We introduce a new inertial iterative scheme …

The current study aims to design an integrated numerical computing-based scheme by
applying the Levenberg–Marquardt backpropagation (LMB) neural network to solve the …

This paper presents a novel approach for solving convex constrained minimization problems
by introducing a special subclass of quasi-nonexpansive operators and combining them …

Purpose A system matrix can be built in order to account for the refractions in an optical
computed tomography (CT) system. In order to utilize this system matrix, iterative methods …

In this paper, a new iterative algorithm with a self-adaptive step size is proposed for split
feasibility problem involving bifunctions and Bregman quasi-nonexpansive mappings. A …

The main purpose of this paper is to study a bilevel split variational inequality problem in two
real Hilbert spaces. We propose a new modified inertial projection and contraction method …

Throughout this study, we present a new algorithm for finding the common solution of a finite
family of monotone inclusion and the fixed point problem of a finite family of generalized …

Objective. Unrolled algorithms are a promising approach for reconstruction of CT images in
challenging scenarios, such as low-dose, sparse-view and limited-angle imaging. In an …

This document presents a (mostly) chronologically-ordered bibliography of scientific
publications on the superiorization methodology and perturbation resilience of algorithms …

The superiorization methodology (SM) is an optimization heuristic in which an iterative
algorithm, which aims to solve a particular problem, is``superiorized''to promote solutions …