Newton-Type Inertial Algorithms for Solving Monotone Equations Governed by Sums of Potential and Nonpotential Operators

Abstract

In a Hilbert space setting, this paper is devoted to the study of a class of first-order algorithms which aim to solve structured monotone equations involving the sum of potential and nonpotential operators. Precisely, we are looking for the zeros of an operator $$A= \nabla f +B $$ , where $$\nabla f$$ is the gradient of a differentiable convex function f, and B is a nonpotential monotone and cocoercive operator. This study is based on the inertial autonomous dynamic previously studied by the authors, which involves dampings controlled respectively by the Hessian of f, and by a Newton-type correction term attached to B. These geometric dampings attenuate the oscillations which occur with the inertial methods with viscous damping. Temporal discretization of this dynamic provides fully splitted proximal-gradient algorithms. Their convergence properties are proven using Lyapunov analysis. These results open the door to the design of first-order accelerated algorithms in numerical optimization taking into account the specific properties of potential and nonpotential terms.