The viscosity technique for the implicit midpoint rule of nonexpansive mappings in Hilbert spaces is established. The strong convergence of this technique is proved under certain assumptions imposed on the sequence of parameters. Moreover, it is shown that the limit solves an additional variational inequality. Applications to variational inequalities, hierarchical minimization problems, and nonlinear evolution equations are included.

We first prove the bounded perturbation resilience for the successive fixed point algorithm of averaged mappings, which extends the string-averaging projection and block-iterative projection methods. We then apply the superiorization methodology to a constrained convex minimization problem where the constraint set is the intersection of fixed point sets of a finite family of averaged mappings.

The split feasibility problem (SFP), which refers to the task of finding a point that belongs to a given nonempty, closed and convex set, and whose image under a bounded linear operator belongs to another given nonempty, closed and convex set, has promising applicability in modeling a wide range of inverse problems. Motivated by the increasingly data-driven regularization in the areas of signal/image processing and statistical learning, in this paper, we study the regularized split feasibility problem (RSFP), which provides a unified model for treating many real-world problems. By exploiting the split nature of the RSFP, we shall gainfully employ several efficient splitting methods to solve the model under consideration. A remarkable advantage of our methods lies in their easier subproblems in the sense that the resulting subproblems have closed-form representations or can be efficiently solved up to a high precision. As an interesting application, we apply the proposed algorithms for finding Dantzig selectors, in addition to demonstrating the effectiveness of the splitting methods through some computational results on synthetic and real medical data sets.

We propose an adaptive way to choose the anchoring parameters for the Halpern iteration to find a fixed point of a nonexpansive mapping in a real Hilbert space. We prove strong convergence of this adaptive Halpern iteration and obtain the rate of asymptotic regularity at least $O(1/k)$ , where $k$ is the number of iterations. Numerical experiments are also provided to show advantages and outperformance of our adaptive Halpern algorithm over the standard Halpern algorithm.