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arXiv · 2602.16402

Primal-dual dynamical systems with closed-loop control for convex optimization in continuous and discrete time

Huan Zhang·Xiangkai Sun·Shengjie Li·Kok Lay Teo
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Abstract

This paper develops a primal-dual dynamical system where the coefficients are designed in closed-loop way for solving a convex optimization problem with linear equality constraints. We first introduce a ``second-order primal" + ``first-order dual'' continuous-time dynamical system, in which both the time scaling and Hessian-driven damping are governed by a feedback control of the gradient for the Lagrangian function. This system achieves the fast convergence rates for the primal-dual gap, the feasibility violation, and the objective residual along its trajectory. Subsequently, by time discretization of this system, we develop an accelerated primal-dual algorithm with a gradient-defined adaptive step size. We also obtain convergence rates for the primal-dual gap, the feasibility violation, and the objective residual. Furthermore, we provide numerical results to demonstrate the practical efficacy and superior performance of the proposed algorithm.

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Huan Zhang, Xiangkai Sun, Shengjie Li, Kok Lay Teo. 2026-02-18. Primal-dual dynamical systems with closed-loop control for convex optimization in continuous and discrete time. https://arxiv.org/abs/2602.16402

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