Strong convergence and bounded perturbation resilience of a modified proximal gradient algorithm

Abstract

Abstract The proximal gradient algorithm is an appealing approach in finding solutions of non-smooth composite optimization problems, which may only has weak convergence in the infinite-dimensional setting. In this paper, we introduce a modified proximal gradient algorithm with outer perturbations in Hilbert space and prove that the algorithm converges strongly to a solution of the composite optimization problem. We also discuss the bounded perturbation resilience of the basic algorithm of this iterative scheme and illustrate it with an application.

Keywords

• Strong convergence
• Bounded perturbation resilience
• Modified proximal gradient algorithm
• Viscosity approximation
• Convex minimization problem