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<p>This paper deals with a Tikhonov regularized second-order dynamical system that incorporates time scaling, asymptotically vanishing damping and Hessian-driven damping for solving convex optimization problems. Under appropriate setting of the parameters, we first obtain fast convergence results of the function value along the trajectory generated by the dynamical system. Then, we show that the trajectory generated by the dynamical system converges weakly to a minimizer of the convex optimization problem. We also demonstrate that, by properly tuning these parameters, both fast convergence rates of the function value and strong convergence of the trajectory towards the minimum norm solution of the convex optimization problem can be achieved simultaneously. Furthermore, we study convergence properties of an inertial proximal gradient algorithm obtained by the temporal discretization of the dynamical system. Finally, we present numerical experiments to illustrate the obtained results.</p>

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Convergence analysis of Tikhonov regularized dynamical systems and inertial algorithms for convex optimization problems

  • 期刊论文

  • 发表日期：

    2026年4月29日

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  • Xiangkai Sun,
  • Guoxiang Tian,
  • Huan Zhang

摘要

This paper deals with a Tikhonov regularized second-order dynamical system that incorporates time scaling, asymptotically vanishing damping and Hessian-driven damping for solving convex optimization problems. Under appropriate setting of the parameters, we first obtain fast convergence results of the function value along the trajectory generated by the dynamical system. Then, we show that the trajectory generated by the dynamical system converges weakly to a minimizer of the convex optimization problem. We also demonstrate that, by properly tuning these parameters, both fast convergence rates of the function value and strong convergence of the trajectory towards the minimum norm solution of the convex optimization problem can be achieved simultaneously. Furthermore, we study convergence properties of an inertial proximal gradient algorithm obtained by the temporal discretization of the dynamical system. Finally, we present numerical experiments to illustrate the obtained results.