A fast continuous time approach for non-smooth convex optimization using Tikhonov regularization technique

Mikhail A. Karapetyants ()
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Mikhail A. Karapetyants: University of Vienna

Computational Optimization and Applications, 2024, vol. 87, issue 2, No 8, 569 pages

Abstract: Abstract In this paper we would like to address the classical optimization problem of minimizing a proper, convex and lower semicontinuous function via the second order in time dynamics, combining viscous and Hessian-driven damping with a Tikhonov regularization term. In our analysis we heavily exploit the Moreau envelope of the objective function and its properties as well as Tikhonov regularization properties, which we extend to a nonsmooth case. We introduce the setting, which at the same time guarantees the fast convergence of the function (and Moreau envelope) values and strong convergence of the trajectories of the system to a minimal norm solution—the element of the minimal norm of all the minimizers of the objective. Moreover, we deduce the precise rates of convergence of the values for the particular choice of parameters. Various numerical examples are also included as an illustration of the theoretical results.

Keywords: Nonsmooth convex optimization; Damped inertial dynamics; Hessian-driven damping; Moreau envelope; Proximal operator; Tikhonov regularization; Strong convergence; 37N40; 46N10; 49M99; 65K05; 65K10; 90C25 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10589-023-00536-6

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