An inertial Tseng's type proximal algorithm for nonsmooth and nonconvex optimization problems

Abstract

We investigate the convergence of a forward-backward-forward proximal-type algorithm with inertial and memory effects when minimizing the sum of a nonsmooth function with a smooth one in the absence of convexity. The convergence is obtained provided an appropriate regularization of the objective satisfies the Kurdyka-Łojasiewicz inequality, which is for instance fulfilled for semi-algebraic functions.

References in corpus (4)

• An inertial alternating direction method of multipliers
• iPiano: Inertial Proximal Algorithm for Non-Convex Optimization
• Inertial Douglas-Rachford splitting for monotone inclusion problems
• An inertial forward-backward-forward primal-dual splitting algorithm for solving monotone inclusion problems

Cited by in corpus (3)

• Inertial primal-dual algorithms for structured convex optimization
• A general inertial proximal point method for mixed variational inequality problem
• An inertial forward-backward algorithm for the minimization of the sum of two nonconvex functions