Yaping Fang

collaborators

3 papers

math.OC2026

Convergence of iterates and improved rates for accelerated augmented Lagrangian methods for linearly constrained convex optimization

Xin He, Nan-Jing Huang, Yi-Bin Xiao +1

Motivated by an inertial primal-dual dynamical system with vanishing damping, we propose a class of accelerated augmented Lagrangian methods with Nesterov extrapolation parameters…

math.OC2026

Trajectory convergence and $o (t^{- 2})$ rates for Nesterov accelerated primal-dual dynamics without Lipschitz gradient assumption

Xin He, Nan-Jing Huang, Yi-Bin Xiao +1

We consider the Nesterov accelerated primal-dual dynamical system \[ \begin{cases} \ddot{x}(t)+\dfracÎ±{t}\dot{x}(t) +\nabla f(x(t)) +A^\top\bigl(Î»(t)+Î¸t\dotÎ»(t)\bigr)+Î²A^\top(…

math.OC2025

Nesterov acceleration for strongly convex-strongly concave bilinear saddle point problems: discrete and continuous-time approaches

Xin He, Ya-Ping Fang

In this paper, we study a bilinear saddle point problem of the form $min_{x} max_{y} F (x) + ⟨ A x, y ⟩ - G (y)$ , where $F$ and $G$ are $μ_{F}$ - and $μ_{G}$ -strongly convex f…

Convergence of iterates and improved rates for accelerated augmented Lagrangian methods for linearly constrained convex optimization

Trajectory convergence and o(t−2) rates for Nesterov accelerated primal-dual dynamics without Lipschitz gradient assumption

Nesterov acceleration for strongly convex-strongly concave bilinear saddle point problems: discrete and continuous-time approaches

Trajectory convergence and $o (t^{- 2})$ rates for Nesterov accelerated primal-dual dynamics without Lipschitz gradient assumption