Iterative algorithms based on the viscosity approximation method for equilibrium and constrained convex minimization problem. (English) Zbl 1397.47018
Summary: The gradient-projection algorithm (GPA) plays an important role in solving constrained convex minimization problems. Based on the viscosity approximation method, we combine the GPA and averaged mapping approach to propose implicit and explicit composite iterative algorithms for finding a common solution of an equilibrium and a constrained convex minimization problem for the first time in this paper. Under suitable conditions, strong convergence theorems are obtained.
MSC:
| 47J25 | Iterative procedures involving nonlinear operators |
| 47J20 | Variational and other types of inequalities involving nonlinear operators (general) |
Keywords:
iterative algorithm; equilibrium problem; constrained convex minimization; variational inequality; strong convergenceReferences:
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