11 citations · 20 across the 15 of their papers we have counts for
15 papers
On some perturbation properties of nonsmooth optimization on Riemannian manifolds with applications
Yuexin Zhou, Chao Ding, Yangjing Zhang
This paper presents a perturbation analysis framework for nonsmooth optimization on connected Riemannian manifolds to bridge the gap between the rapid development of algorithmic ap…
Strong Variational Sufficiency for Nonlinear Semidefinite Programming and its Implications
Shiwei Wang, Chao Ding, Yangjing Zhang +1
Strong variational sufficiency is a newly proposed property, which turns out to be of great use in the convergence analysis of multiplier methods. However, what this property impli…
Robins-Monro Augmented Lagrangian Method for Stochastic Convex Optimization
Rui Wang, Chao Ding
In this paper, we propose a Robbins-Monro augmented Lagrangian method (RMALM) to solve a class of constrained stochastic convex optimization, which can be regarded as a hybrid of t…
On the robust isolated calmness of a class of nonsmooth optimizations on Riemannian manifolds and its applications
Chenglong Bao, Chao Ding, Yuexin Zhou
This paper studies the robust isolated calmness property of the KKT solution mapping of a class of nonsmooth optimization problems on Riemannian manifolds. The manifold versions of…
Local convergence analysis of augmented Lagrangian method for nonlinear semidefinite programming
Shiwei Wang, Chao Ding
The augmented Lagrangian method (ALM) has gained tremendous popularity for its elegant theory and impressive numerical performance since it was proposed by Hestenes and Powell in 1…
A Semismooth Newton based Augmented Lagrangian Method for Nonsmooth Optimization on Matrix Manifolds
Yuhao Zhou, Chenglong Bao, Chao Ding +1
This paper is devoted to studying an augmented Lagrangian method for solving a class of manifold optimization problems, which have nonsmooth objective functions and nonlinear const…