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…
Matrix optimization based Euclidean embedding with outliers
Qian Zhang, Xinyuan Zhao, Chao Ding
Euclidean embedding from noisy observations containing outlier errors is an important and challenging problem in statistics and machine learning. Many existing methods would strugg…
A proximal DC approach for quadratic assignment problem
Zhuoxuan Jiang, Xinyuan Zhao, Chao Ding
In this paper, we show that the quadratic assignment problem (QAP) can be reformulated to an equivalent rank constrained doubly nonnegative (DNN) problem. Under the framework of th…
Nonsmooth Composite Matrix Optimization: Strong Regularity, Constraint Nondegeneracy and Beyond
Ying Cui, Chao Ding
The nonsmooth composite matrix optimization problem (CMatOP), in particular, the matrix norm minimization problem, is a generalization of the matrix conic programming problem with…
Spectral operators of matrices: semismoothness and characterizations of the generalized Jacobian
Chao Ding, Defeng Sun, Jie Sun +1
Spectral operators of matrices proposed recently in [C. Ding, D.F. Sun, J. Sun, and K.C. Toh, Math. Program. {\bf 168}, 509--531 (2018)] are a class of matrix valued functions, whi…
Quadratic growth conditions for convex matrix optimization problems associated with spectral functions
Ying Cui, Chao Ding, Xinyuan Zhao
In this paper, we provide two types of sufficient conditions for ensuring the quadratic growth conditions of a class of constrained convex symmetric and non-symmetric matrix optimi…
Variational Analysis of the Ky Fan -norm
Chao Ding
In this paper, we will study some variational properties of the Ky Fan -norm of matrices, which are closed related to a class of basic nonlinear optimization…
Characterization of the Robust Isolated Calmness for a Class of Conic Programming Problems
Chao Ding, Defeng Sun, Liwei Zhang
This paper is devoted to studying the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) solution mapping for a large class of interesting conic programming problems (includi…
Convex Optimization Learning of Faithful Euclidean Distance Representations in Nonlinear Dimensionality Reduction
Chao Ding, Hou-Duo Qi
Classical multidimensional scaling only works well when the noisy distances observed in a high dimensional space can be faithfully represented by Euclidean distances in a low dimen…
Spectral Operators of Matrices
Chao Ding, Defeng Sun, Jie Sun +1
The class of matrix optimization problems (MOPs) has been recognized in recent years to be a powerful tool by researchers far beyond the optimization community to model many import…