An accelerated viscosity forward-backward splitting algorithm with the linesearch process for convex minimization problems

In this paper, we consider and investigate a convex minimization problem of the sum of two convex functions in a Hilbert space. The forward-backward splitting algorithm is one of the popular optimization methods for approximating a minimizer of the function; however, the stepsize of this algorithm depends on the Lipschitz constant of the gradient of the function, which is not an easy work to find in general practice. By using a new modification of the linesearches of Cruz and Nghia [Optim. Methods Softw. 31:1209-1238, 2016] and Kankam et al. [Math. Methods Appl. Sci. 42:1352-1362, 2019] and an inertial technique, we introduce an accelerated viscosity-type algorithm without any Lipschitz continuity assumption on the gradient. A strong convergence result of the proposed algorithm is established under some control conditions. As applications, we apply our algorithm to solving image and signal recovery problems. Numerical experiments show that our method has a higher efficiency than the well-known methods in the literature.

Automated summary

This study introduces an accelerated viscosity-type algorithm for solving a convex minimization problem of the sum of two convex functions in a Hilbert space. The algorithm does not require any Lipschitz continuity assumption on the gradient and establishes a strong convergence result under some control conditions. Numerical experiments demonstrate that the method is more efficient than well-known methods in the literature.