Accelerated primal--dual dynamics and algorithms for convex optimization with nonlinear inequality constraints
We consider convex optimization with nonlinear inequality constraints and develop a primal--dual multiplier framework that is consistent in continuous and discrete time. We first propose continuous-time dynamics with Nesterov-type vanishing damping $\alpha/t$, together with suitable extrapolations of the dual variable and the nonlinear constraint mapping. Under convexity assumptions and $\alpha\geq3$, we establish $\mathcal O(t^{-2})$ convergence rates for both nonlinear feasibility and the objective residual. We then derive an inexact accelerated primal--dual algorithm through a compatible discretization of a perturbed version of the dynamics. For composite convex objectives, a weighted summability condition on the primal inexactness yields the $\mathcal O(k^{-2})$ rates for feasibility and the objective residual, thereby matching the accelerated rates of their continuous-time counterparts. To the best of our knowledge, this is the first Nesterov-type primal--dual multiplier framework for convex optimization with nonlinear inequality constraints.