Some recent talks

A majorized proximal point dual Newton algorithm for nonconvex statistical optimization problems (The Sixth International Conference on Continuous Optimization, Technical University (TU) of Berlin, Germany, August 3–8, 2019).

Matrix Cones and Spectral Operators of Matrices (Advances in the Geometric and Analytic Theory of Convex Cones, Sungkyunkwan University, Korea, May 27–31, 2019).

On the Relationships of ADMM and Proximal ALM for Convex Optimization Problems (Institute of Applied Physics and Computational Mathematics, Beijing, April 12, 2019).

Sparse semismooth Newton methods and big data composite optimization (New Computing-Driven Opportunities for Optimization, Wuyishan, August 13-17, 2018).

On the efficient computation of the projector over the Birkhoff polytope (International Symposium on Mathematical Programming 2018, Bordeaux, July 1-6, 2018).

A block symmetric Gauss-Seidel decomposition theorem and its applications in big data nonsmooth optimization (International Workshop on Modern Optimization and Applications, AMSS, Beijing, June 16-18, 2018).

On the Equivalence of Inexact Proximal ALM and ADMM for a Class of Convex Composite Programming(DIMACS Workshop on ADMM and Proximal Splitting Methods in Optimization, Rutgers University, June 11-13, 2018).

A block symmetric Gauss-Seidel decomposition theorem and its applications in big data nonsmooth optimization (The Hong Kong Mathematical Society Annual General meeting 2018, May 26, 2018).

SDPNAL+: A MATLAB software package for large-scale SDPs with a user-friendly interface (SIAM-ALA18, May 2018).

Second order sparsity and big data optimization (October 2017).

Error bounds and the superlinear convergence rates of the augmented Lagrangian methods (October 2017).

Block symmetric Gauss-Seidel iteration and multi-block semidefnite programming (October 2017).

A two-phase augmented Lagrangian approach for linear and convex quadratic semidefinite programming problems(December 2016).

Linear rate convergence of the ADMM for multi-block convex conic programming (August 2016).

An efficient inexact accelerated block coordinate descent method for least squares semidefinite programming (June 2015).

Multi-stage convex relaxation approach for low-rank structured PSD matrix recovery (May 2014).