Convergence Rates of Inertial Primal-Dual Dynamical Methods for Separable Convex Optimization Problems. - Xin He 020; Rong Hu; Ya-Ping Fang (SIAM J. Control. Optim., 2021)

Fast primal-dual algorithm via dynamical system for a linearly constrained convex optimization problem. - Xin He 020; Rong Hu; Ya-Ping Fang (Autom., 2022)

Inertial accelerated primal-dual methods for linear equality constrained convex optimization problems. - Xin He 020; Rong Hu; Ya-Ping Fang (Numer. Algorithms, 2022)

"Second-Order Primal" + "First-Order Dual" Dynamical Systems With Time Scaling for Linear Equality Constrained Convex Optimization Problems. - Xin He 020; Rong Hu; Ya-Ping Fang (IEEE Trans. Autom. Control., 2022)

Inertial accelerated augmented Lagrangian algorithms with scaling coefficients to solve exactly and inexactly linearly constrained convex optimization problems. - Xin He 020; Rong Hu; Ya-Ping Fang (J. Comput. Appl. Math., 2025)

Accelerated quadratic penalty dynamic approaches with applications to distributed optimization. - Xin He 020; Luyao Guo; Dong He (Neural Networks, 2025)

A General Mixed-Order Primal-Dual Dynamical System with Tikhonov Regularization. - Hong-lu Li; Rong Hu; Xin He 020; Yi-bin Xiao (J. Optim. Theory Appl., 2025)

Accelerated primal-dual methods for strongly convex objective functions in continuous and discrete time. - Xin He 020; Dong He; Ya-Ping Fang (Autom., 2026)