Chao Ding
Chao Ding
Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of
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Cited by
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An introduction to a class of matrix cone programming
C Ding, D Sun, KC Toh
Mathematical Programming, 2012
First order optimality conditions for mathematical programs with semidefinite cone complementarity constraints
C Ding, D Sun, JY Jane
Mathematical Programming, 2013
Characterization of the robust isolated calmness for a class of conic programming problems
C Ding, D Sun, L Zhang
SIAM Journal on Optimization 27 (1), 67-90, 2017
On the Moreau-Yosida regularization of the vector k-norm related functions
B Wu, C Ding, D Sun, KC Toh
SIAM Journal on Optimization 24 (2), 766-794, 2014
An Introduction to a Class of Matrix Optimization Problems
C Ding
National University of Singapore, 2012
Quadratic growth conditions for convex matrix optimization problems associated with spectral functions
Y Cui, C Ding, X Zhao
SIAM Journal on Optimization 27 (4), 2332-2355, 2017
Spectral operators of matrices
C Ding, D Sun, J Sun, KC Toh
Mathematical Programming 168 (1), 509-531, 2018
Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction
C Ding, HD Qi
Mathematical Programming 164 (1), 341-381, 2017
Convex Euclidean Distance Embedding for Collaborative Position Localization with NLOS Mitigation
C Ding, HD Qi
Computational Optimization and Applications 66 (1), 187-218, 2017
Variational Analysis of the Ky Fan k-norm
C Ding
Set-Valued and Variational Analysis, 2016
Spectral operators of matrices: semismoothness and characterizations of the generalized Jacobian
C Ding, D Sun, J Sun, KC Toh
SIAM Journal on Optimization 30 (1), 630-659, 2020
A semismooth Newton based augmented Lagrangian method for nonsmooth optimization on matrix manifolds
Y Zhou, C Bao, C Ding, J Zhu
Mathematical Programming, 1-61, 2022
A proximal DC approach for quadratic assignment problem
Z Jiang, X Zhao, C Ding
Computational Optimization and Applications 78, 825–851, 2021
Augmented Lagrangian methods for convex matrix optimization problems
Y Cui, C Ding, XD Li, XY Zhao
Journal of the Operations Research Society of China 10 (2), 305-342, 2022
THOR, Trace-Based Hardware-Driven Layer-Oriented Natural Gradient Descent Computation
M Chen, K Gao, X Liu, Z Wang, N Ni, Q Zhang, L Chen, C Ding, Z Huang, ...
Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 7046-7054, 2021
Local convergence analysis of augmented Lagrangian method for nonlinear semidefinite programming
S Wang, C Ding
arXiv preprint arXiv:2110.10594, 2021
A computable characterization of the extrinsic mean of reflection shapes and its asymptotic properties
C Ding, HD Qi
Asia-Pacific Journal of Operational Research 32 (01), 1540005, 2015
Strong Variational Sufficiency for Nonlinear Semidefinite Programming and its Implications
S Wang, C Ding, Y Zhang, X Zhao
arXiv preprint arXiv:2210.04448, 2022
Robins-Monro Augmented Lagrangian Method for Stochastic Convex Optimization
R Wang, C Ding
arXiv preprint arXiv:2208.14019, 2022
On the robust isolated calmness of a class of nonsmooth optimizations on Riemannian manifolds and its applications
Y Zhou, C Bao, C Ding
arXiv preprint arXiv:2208.07518, 2022
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