**Google Scholar**

**ResearchGate**

## Preprints

- F.X.Y Feng, C. Ding and X.D. Li, A quadratically convergent semismooth Newton method for nonlinear semidefinite programming without the subdifferential regularity, February 2024, arXiv:2402.13814.
- Y.X. Zhou, C. Ding and Y.J. Zhang, Strong variational sufficiency of nonsmooth optimization problems on Riemannian manifolds, August 2023, arXiv:2308.06793.
- C. Ding and H.D. Qi, An optimization study of diversification return portfolios, February 2023, arXiv: 2303.01657.
- R. Wang and C. Ding, Robins-Monro augmented Lagrangian method for stochastic convex optimization, August 2022, arXiv: 2208.14019.
- 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.
- Y. Cui and C. Ding, Nonsmooth composite matrix optimization: strong regularity, constraint nondegeneracy and beyond, July 2019, arXiv: 1907.13253.

## Selected publications

- S.W. Wang, C. Ding, Y.J. Zhang, and X.Y. Zhao, Strong variational sufficiency for nonlinear semidefinite programming and its implications,
, 33, 2988-3011 (2023). Revision 2 May 2023, Revision 1 February 2023, October 2022, arXiv: 2210.04448.*SIAM Journal on Optimization* - S.W. Wang and C. Ding, Local convergence analysis of augmented Lagrangian method for nonlinear semidefinite programming,
, 87, 39-81 (2024), DOI:10.1007/s10589-023-00520-0. Revision 2 July 2023, Revision 1 August 2022, October 2021, arXiv: 2110.10594.*Computational Optimization and Applications* - Y.H. Zhou, C.L. Bao, C. Ding, and J. Zhu, A semismooth Newton based augmented Lagrangian method for nonsmooth optimization on matrix manifolds,
, 201, 1-61 (2023), DOI:10.1007/s10107-022-01898-1.**Mathematical Programming****Code:**, March 2021, Revision 1 November 2021, Revision 2 July 2022, arXiv: 2103.02855.**Almssn** - Y. Cui, C. Ding, X.D. Li, and X.Y. Zhao, Augmented Lagrangian methods for convex matrix optimization problems,
, 10, 305–342 (2022).*Journal of the Operations Research Society of**China* - Q. Zhang, X.Y. Zhao and C. Ding, Matrix optimization based Euclidean embedding with outliers,
, 79, 235-271 (2021), arXiv: 2012.12772.*Computational Optimization and Applications* - 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). - Z.X. Jiang, X.Y. Zhao and C. Ding, A proximal DC approach for quadratic assignment problem,
78, 825-851 (2021), arXiv:1908.04522.**Computational Optimization and Applications,** - C. Ding, D.F. Sun, J. Sun and K.C. Toh, Spectral operators of matrices: semismoothness and characterizations of the generalized Jacobian,
, 30, 630–659 (2020), arXiv: 1810.09856. Revised from the second part of arXiv: 1401.2269, January 2014.*SIAM Journal on Optimization* - C. Ding, D.F. Sun, J. Sun and K.C. Toh, Spectral operator of matrices,
, 168, 509–531 (2018). Revised from the first part of arXiv: 1401.2269, January 2014.**Mathematical Programming** - Y. Cui, C. Ding and X.Y. Zhao, Quadratic growth conditions for convex matrix optimization problems associated with spectral functions,
, 27, 2332–2355 (2017).**SIAM Journal on Optimization** - C. Ding, D.F. Sun and L.W. Zhang, Characterization of the robust isolated calmness for a class of conic programming problems,
, 27, 67–90 (2017).**SIAM Journal on Optimization** - C. Ding and H.D. Qi, Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction,
, 164, 341–381 (2017).**Mathematical Programming** - C. Ding, Variational analysis of the Ky Fan k-norm,
, 25, 265–296 (2017).**Set-Valued and Variational Analysis** - C. Ding and H.D. Qi, Convex euclidean distance embedding for collaborative position localization with NLOS mitigation,
, 66, 187–218 (2017).**Computational Optimization and Applications** - C. Ding and H.D. Qi, A computable characterization of the extrinsic mean of reflection shapes and its asymptotic properties,
, 32, 1540005 (2015).**Asia-Pacific Journal of Operational Research** - B. Wu, C. Ding, D.F. Sun and K.C. Toh, On the Moreau-Yosida regularization of the vector k-norm related functions,
, 24, 766–794 (2014).**SIAM Journal on Optimization** - C. Ding, D.F. Sun and J.J. Ye, First order optimality conditions for mathematical programs with semidefinite cone complementarity constraints,
,147, 539–579 (2014).**Mathematical Programming** - C. Ding, D.F. Sun and K.C. Toh, An introduction to a class of matrix cone programming,
**Mathematical Programming**144, 141–179 (2014).**,**

## Thesis of Students

- “Perturbation analysis of composite matrix optimization and its applications”
**Ph.D. Thesis of Shiwei Wang,**June 2023 (Chinese version).

## Selected Talks

- A quadratically convergent semismooth Newton method for nonlinear semidefinite programming without strong regularity, Nonsmooth Optimization and Variational Analysis 2023, The Hong Kong Polytechnic University, Hong Kong, China.
- Nonsmooth optimization over Riemannian manifolds: Manifold strong variational sufficiency and Riemannian ALM, Optimization, Equilibrium and Complementarity 2023, The Hong Kong Polytechnic University, Hong Kong, China.
- On the convergence analysis of augmented Lagrangian method for matrix optimization: Strong variational sufficiency for nonlinear SDP, Optimization in the Big Data Era 2022, National University of Singapore, Singapore.
- 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.