The split feasibility problem is to find a point x∗ with the property that x∗∈ C and A x∗∈ Q,
where C and Q are nonempty closed convex subsets of real Hilbert spaces X and Y …

Inspired by the works of López et al.[21] and the recent paper of Dang et al.[15], we devise a
new inertial relaxation of the CQ algorithm for solving Split Feasibility Problems (SFP) in real …

In this paper, first, we review the projection and contraction methods for solving the split
feasibility problem (SFP), and then by using the inverse strongly monotone property of the …

In Hilbert spaces, we study the split feasibility problem with multiple output sets for
demicontractive mappings. For solving this problem, we propose an iterative method and …

In this paper, we propose an alternated inertial general splitting method with linearization for
a split feasibility problem. Four rules of inertial parameters and relaxation parameters are …

In this paper, we propose a CQ-type algorithm for solving the split feasibility problem (SFP)
in real Hilbert spaces. The algorithm is designed such that the step-sizes are directly …

In this article, we introduce a general splitting method with linearization to solve the split
feasibility problem and propose a way of selecting the stepsizes such that the …

The split feasibility problem (SFP) is to find so that, where C and Q are non-empty closed
convex subsets in Hilbert spaces and, respectively, and A is a linear bounded operator from …

In this paper, we introduce a new relaxed method for solving the split feasibility problem in
Hilbert spaces. In our method, the projection to the halfspace is replaced by the one to the …

We propose a novel adaptive stepsize for the gradient descent scheme to solve
unconstrained nonlinear optimization problems. With the convex and smooth objective …