Publications

Google Scholar

ResearchGate

Preprints

1. S.W. Wang and C. Ding, Local convergence analysis of augmented Lagrangian method for nonlinear semidefinite programming, 1-34, October 2021, arXiv:2110.10594.
2. 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.
3. Y. Cui and C. Ding, Nonsmooth composite matrix optimization: strong regularity, constraint nondegeneracy and beyond, July 2019, arXiv: 1907.13253.

---

Selected publications

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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).
8. 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).
9. C. Ding and H.D. Qi, Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction, Mathematical Programming, 164, 341–381 (2017).
10. C. Ding, Variational analysis of the Ky Fan k-norm, Set-Valued and Variational Analysis, 25, 265–296 (2017).
11. 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).
12. 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).
13. 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).
14. 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).
15. C. Ding, D.F. Sun and K.C. Toh, An introduction to a class of matrix cone programming, Mathematical Programming, 144, 141–179 (2014).