Publications

Google Scholar

ResearchGate

Preprints

  1. F.X.Y Feng, C. Ding and X.D. LiA quadratically convergent semismooth Newton method for nonlinear semidefinite programming without the subdifferential regularity, February 2024, arXiv:2402.13814.
  2. Y.X. Zhou, C. Ding and Y.J. Zhang, Strong variational sufficiency of nonsmooth optimization problems on Riemannian manifolds, August 2023, arXiv:2308.06793.
  3. C. Ding and H.D. Qi, An optimization study of diversification return portfolios, February 2023, arXiv: 2303.01657.
  4. R. Wang and C. Ding, Robins-Monro augmented Lagrangian method for stochastic convex optimization, August 2022, arXiv: 2208.14019.
  5. Y.X. Zhou, C.L. Bao and C. Ding, On the robust isolated calmness of a class of nonsmooth optimizations on Riemannian manifolds and its applications, August 2022, arXiv: 2208.07518.
  6. Y. Cui and C. Ding, Nonsmooth composite matrix optimization: strong regularity, constraint nondegeneracy and beyond, July 2019, arXiv: 1907.13253.

Selected publications

  1. S.W. Wang, C. Ding, Y.J. Zhang, and X.Y. Zhao, Strong variational sufficiency for nonlinear semidefinite programming and its implications, SIAM Journal on Optimization, 33, 2988-3011 (2023). Revision 2 May 2023,  Revision 1 February 2023October 2022, arXiv: 2210.04448.
  2. S.W. Wang and C. Ding, Local convergence analysis of augmented Lagrangian method for nonlinear semidefinite programming, Computational Optimization and Applications, 87, 39-81 (2024), DOI:10.1007/s10589-023-00520-0. Revision 2 July 2023, Revision 1 August 2022, October 2021, arXiv: 2110.10594.
  3. Y.H. Zhou, C.L. Bao, C. Ding, and J. Zhu, A semismooth Newton based augmented Lagrangian method for nonsmooth optimization on matrix manifolds, Mathematical Programming, 201, 1-61 (2023), DOI:10.1007/s10107-022-01898-1. Code: Almssn, March 2021, Revision 1 November 2021, Revision 2 July 2022arXiv: 2103.02855.
  4. 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, 10, 305–342 (2022).
  5. 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.
  6. 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 (AAAI21)35(8), 7046-7054 (2021).
  7. 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.
  8. C. Ding, D.F. Sun, J. Sun and K.C. Toh, Spectral operators of matrices: semismoothness and characterizations of the generalized JacobianSIAM Journal on Optimization, 30, 630–659 (2020), arXiv: 1810.09856. Revised from the second part of arXiv: 1401.2269, January 2014.
  9. 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.
  10. 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).
  11. 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).
  12. C. Ding and H.D. Qi, Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction, Mathematical Programming, 164, 341–381 (2017).
  13. C. Ding, Variational analysis of the Ky Fan k-norm, Set-Valued and Variational Analysis, 25, 265–296 (2017).
  14. 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).
  15. 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).
  16. 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).
  17. 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).
  18. C. Ding, D.F. Sun and K.C. Toh, An introduction to a class of matrix cone programmingMathematical Programming144, 141–179 (2014).

Thesis of Students