Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping

TL;DR

This paper introduces a novel inertial gradient-like dynamical system with vanishing damping for multiobjective optimization, proving solution existence, convergence to Pareto optima, and a fast convergence rate, advancing optimization theory.

Contribution

It presents the first inertial multiobjective system with asymptotic vanishing damping, extending prior work and establishing convergence and rate results for such systems.

Findings

Solutions exist in finite dimensions.

Bounded solutions converge weakly to Pareto optima.

Function values converge at rate O(t^{-2}).

Abstract

We present a new gradient-like dynamical system related to unconstrained convex smooth multiobjective optimization which involves inertial effects and asymptotic vanishing damping. To the best of our knowledge, this system is the first inertial gradient-like system for multiobjective optimization problems including asymptotic vanishing damping, expanding the ideas laid out in [H. Attouch and G. Garrigos, Multiobjective optimization: an inertial approach to Pareto optima, preprint, arXiv:1506.02823, 201]. We prove existence of solutions to this system in finite dimensions and further prove that its bounded solutions converge weakly to weakly Pareto optimal points. In addition, we obtain a convergence rate of order $O(t^{-2})$ for the function values measured with a merit function. This approach presents a good basis for the development of fast gradient methods for multiobjective optimization.