- S.W. Wang and C. Ding, Local convergence analysis of augmented Lagrangian method for nonlinear semidefinite programming, 1-34, October 2021, arXiv:2110.10594.
- Y.H. Zhou, C.L. Bao, C. Ding and J. Zhu, A semismooth Newton based augmented Lagrangian method for nonsmooth optimization on matrix manifolds, March 2021, Revision, November 2021, arXiv: 2103.02855.
- Y. Cui and C. Ding, Nonsmooth composite matrix optimization: strong regularity, constraint nondegeneracy and beyond, July 2019, arXiv: 1907.13253.
- Y. Cui, C. Ding, X.D. Li and X.Y. Zhao, Augmented Lagrangian methods for convex matrix optimization problems, Journal of the Operations Research Society of China, DOI: 10.1007/s40305-021-00346-9, 2021.
- Q. Zhang, X.Y. Zhao and C. Ding, Matrix optimization based Euclidean embedding with outliers, Computational Optimization and Applications, 79, 235-271 (2021), arXiv: 2012.12772.
- M.Y. Chen, K.X. Gao, X.L. Liu, Z.D. Wang, N.X. Ni, Q. Zhang, L. Chen, C. Ding, Z.H. Huang, M. Wang, S.L. Wang, F. Yu, X.Y. Zhao and D.C. Xu, THOR, Trace-Based Hardware-Driven Layer-Oriented Natural Gradient Descent Computation, Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 7046-7054. 2021.
- Z.X. Jiang, X.Y. Zhao and C. Ding, A proximal DC approach for quadratic assignment problem, Computational Optimization and Applications, 78, 825-851 (2021), arXiv:1908.04522.
- C. Ding, D.F. Sun, J. Sun and K.C. Toh, Spectral operators of matrices: semismoothness and characterizations of the generalized Jacobian, SIAM Journal on Optimization, 30, 630–659 (2020), arXiv: 1810.09856. Revised from the second part of arXiv: 1401.2269, January 2014.
- C. Ding, D.F. Sun, J. Sun and K.C. Toh, Spectral operator of matrices, Mathematical Programming, 168, 509–531 (2018). Revised from the first part of arXiv: 1401.2269, January 2014.
- Y. Cui, C. Ding and X.Y. Zhao, Quadratic growth conditions for convex matrix optimization problems associated with spectral functions, SIAM Journal on Optimization, 27, 2332–2355 (2017).
- C. Ding, D.F. Sun and L.W. Zhang, Characterization of the robust isolated calmness for a class of conic programming problems, SIAM Journal on Optimization, 27, 67–90 (2017).
- C. Ding and H.D. Qi, Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction, Mathematical Programming, 164, 341–381 (2017).
- C. Ding, Variational analysis of the Ky Fan k-norm, Set-Valued and Variational Analysis, 25, 265–296 (2017).
- C. Ding and H.D. Qi, Convex euclidean distance embedding for collaborative position localization with NLOS mitigation, Computational Optimization and Applications, 66, 187–218 (2017).
- C. Ding and H.D. Qi, A computable characterization of the extrinsic mean of reflection shapes and its asymptotic properties, Asia-Pacific Journal of Operational Research, 32, 1540005 (2015).
- B. Wu, C. Ding, D.F. Sun and K.C. Toh, On the Moreau-Yosida regularization of the vector k-norm related functions, SIAM Journal on Optimization, 24, 766–794 (2014).
- C. Ding, D.F. Sun and J.J. Ye, First order optimality conditions for mathematical programs with semidefinite cone complementarity constraints, Mathematical Programming,147, 539–579 (2014).
- C. Ding, D.F. Sun and K.C. Toh, An introduction to a class of matrix cone programming, Mathematical Programming, 144, 141–179 (2014).
- Local convergence analysis of augmented Lagrangian method for nonlinear semidefinite programming, MOS2021, Qingdao, China.
- Perturbation analysis of matrix optimization, ICCOPT2019, Berlin, Germany.
- Matrix optimization: recent progress on algorithm foundation, ISMP2018, Bordeaux, France.
- Characterization of the Robust Isolated Calmness for a Class of Conic Programming Problems, SIAM OP17, Vancouver, Canada.
- Convex Optimization Learning of Faithful Euclidean Distance Representations in Nonlinear Dimensionality Reduction, ICCOPT 2016, Tokyo, Japan.
- First Order Optimality Conditions for Mathematical Programs with SDP Cone Complementarity Constraints, ISMP2012, Berlin, Germany.
- An Introduction to a Class of Matrix Cone Programming, SIAM OP11, Darmstadt, Germany.