Abstract

In a Hilbert space mathcal{H}, we study a dynamic inertial Newton method which aims to solve additively structured monotone equations involving the sum of potential and nonpotential terms. Precisely, we are looking for the zeros of an operator A= nabla f +B , where ∇f is the gradient of a continuously differentiable convex function f and B is a nonpotential monotone and cocoercive operator. Besides a viscous friction term, the dynamic involves geometric damping terms which are controlled respectively by the Hessian of the potential f and by a Newton-type correction term attached to B. Based on a fixed point argument, we show the well-posedness of the Cauchy problem. Then we show the weak convergence as tto +infty of the generated trajectories towards the zeros of nabla f +B. The convergence analysis is based on the appropriate setting of the viscous and geometric damping parameters. The introduction of these geometric dampings makes it possible to control and attenuate the known oscillations for the viscous damping of inertial methods. Rewriting the second-order evolution equation as a first-order dynamical system enables us to extend the convergence analysis to nonsmooth convex potentials. These results open the door to the design of new first-order accelerated algorithms in optimization taking into account the specific properties of potential and nonpotential terms. The proofs and techniques are original and differ from the classical ones due to the presence of the nonpotential term.

Highlights

  • Introduction and preliminary results LetH be a real Hilbert space endowed with the scalar product ·, · and the associated norm ·

  • Many situations coming from physics, biology, human sciences involve equations containing both potential and nonpotential terms

  • We will consider continuous inertial dynamics whose solution trajectories converge as t → +∞ to solutions of (1.1)

Read more

Summary

Adly et al Fixed Point Theory Algorithms Sci Eng

In the above equation, ∇f is the gradient of a convex continuously differentiable function f : H → R (that’s the potential part), and B : H → H is a nonpotential operator 1 which is supposed to be monotone and cocoercive. To this end, we will consider continuous inertial dynamics whose solution trajectories converge as t → +∞ to solutions of (1.1). The analysis of the algorithmic part and its link with first-order numerical optimization will be carried out in a second companion paper From this perspective, damped inertial dynamics offer a natural way to accelerate these systems.

Likewise the term
We obtain
Therefore we end up with the condition γ
From we also get that
This in turn is equivalent to
We have
The solution set S is now defined by
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call