Mathematical Optimization for Data Science Group, Saarland University

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Department of Mathematics and Computer Science, Saarland University, Germany

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Continuous Newton-like Methods featuring Inertia and Variable Mass

C. Castera, H. Attouch, J. Fadili and P. Ochs

Abstract:
We introduce a new dynamical system, at the interface between second-order dynamics with inertia and Newton's method. This system extends the class of inertial Newton-like dynamics by featuring a time-dependent parameter in front of the acceleration, called variable mass. For strongly convex optimization, we provide guarantees on how the Newtonian and inertial behaviors of the system can be non-asymptotically controlled by means of this variable mass. A connection with the Levenberg-Marquardt (or regularized Newton's) method is also made. We then show the effect of the variable mass on the asymptotic rate of convergence of the dynamics, and in particular, how it can turn the latter into an accelerated Newton method. We provide numerical experiments supporting our findings. This work represents a significant step towards designing new algorithms that benefit from the best of both first- and second-order optimization methods.



pdf	Bibtex	Publisher's link	arXiv

Latest update: 31.01.2023

Citation:
C. Castera, H. Attouch, J. Fadili, P. Ochs:
Continuous Newton-like Methods featuring Inertia and Variable Mass.

SIAM Journal on Optimization, 34(1):251-277, 2024.

Bibtex:
@article{CAFO24, title = {Continuous Newton-like Methods featuring Inertia and Variable Mass}, author = {C. Castera and H. Attouch and J. Fadili and P. Ochs}, year = {2024}, journal = {SIAM Journal on Optimization}, number = {1}, volume = {34}, pages = {251--277} }

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