Methods for solving constrained convex minimization problems and finding zeros of the sum of two operators in Hilbert spaces

In this paper, let H be a real Hilbert space and let C be a nonempty, closed, and convex subset of H. We assume that , where is an alpha-inverse-strongly monotone mapping, is a maximal monotone operator, the domain of B is included in C. Let U denote the solution set of the constrained convex minimization problem. Based on the viscosity approximation method, we use a gradient-projection algorithm to propose composite iterative algorithms and find a common solution of the problems which we studied. Then we regularize it to find a unique solution by gradient-projection algorithm. The point which we find solves the variational inequality , . Under suitable conditions, the constrained convex minimization problem can be transformed into the split feasibility problem. Zeros of the sum of two operators can be transformed into the variational inequality problem and the fixed point problem. Furthermore, new strong convergence theorems and applications are obtained in Hilbert spaces, which are useful in nonlinear analysis and optimization.