Presidential Chair Professor

Education Background

Ph.D. Applied Mathematics, SUNY, 1987

B.S. Environmental Engineering, Chongqing University, 1977

Research Field
Numerical Optimization, Algorithm Design, Analysis and Implementation, Applications of Optimization in Various Areas, particularly in Data Science, Image/Signal Processing and Machine Learning
Personal Website

Professor Zhang Yin is the Presidential Chair Professor of the Chinese University of Hong Kong, Shenzhen. He is also a tenured professor in the department of computing and applied mathematics at Rice University. Professor Zhang graduated from Chongqing University with a bachelor's degree and from The State University of New York at Stony Brook with a doctoral degree.

Professor Zhang Yin's main research areas are optimization algorithm design, analysis, implementation, as well as various practical applications and corresponding computer software development. Two examples may show his academic achievements. First, one of the most important advances in the field of optimization algorithms in the last thirty years is the study of interior point method. This method has become the most reliable and accurate algorithm to solve the general convex optimization problem. Professor Zhang has made a series of world-leading works in the research of inner point method, including the first inner point method super linear convergence velocity theory and the non-feasible point iterative convergence theory and other major theoretical breakthroughs. On the other hand, LIPSOL, the linear programming software he designed and developed, was selected as the official software by MATLAB, the most authoritative scientific and engineering computing platform, and the copyright was purchased, which was widely used by thousands of MATLAB users around the world for a long time. Second, for more than a decade, the team led by Professor Zhang Yin has made world-leading achievements in image and signal processing algorithms and data compressed sensing algorithms, among which the number of citations of the three most influential papers has reached an average of over 1,000 times in 2017.

The most successful example of Professor Zhang's application of optimization in practice came in collaboration with NASA's optimal spin path for spacecraft. Thrust-free rotation of spacecraft is the optimal attitude and trajectory for spacecraft rotation control without the use of fuel thrusters. Under the guidance of Professor Zhang and his collaborators, Professor Zhang’s students successfully solved the calculation of the optimal control trajectory, enabling propeller-free rotations on the International Space Station twice in 2006 and 2007. These propeller-free rotations have saved the cost of transporting fuel to the International Space Station, with estimated economic benefits in millions of dollars.

Professor Zhang Yin has published more than 80 academic papers in top international peer-reviewed journals, and has won numerous best papers award from relevant associations and journals at home and abroad. He is invited to deliver hundreds of reports at academic conferences or academic research institutions. Since the 1990s, Professor Zhang's research projects have received millions of dollars in grants from the US National Science Foundation and other government and nongovernmental organizations. Professor Zhang has supervised nearly 30 master's and doctoral students and postdocs. At Rice University, he was the first professor to receive the Presidential Award for Outstanding Supervisor.

Academic Publications

1. Lijun Xu, Bo Yu and Yin Zhang. An Alternating Direction and Projection Algorithm for Structure-enforced Matrix Factorization. Computational Optimization and Applications (2017). First Online: 24 April, 2017. doi: 10.1007/s10589-017-9913-x.

2. Zaiwen Wen and Yin Zhang. Accelerating Convergence by Augmented Rayleigh-Ritz Projections For Large-Scale Eigenpair Computation. SIAM Journal on Matrix Analysis and Applications. Vol. 38-2, pp. 273-296. 2017.

3. Junyu Zhang, Zaiwen Wen and Yin Zhang. Subspace Methods With Local Refinements for Eigenvalue Computation Using Low-Rank Tensor-Train Format. Journal of Scientific Computing (First online 2016). doi: 10.1007/s10915-016-0255-0. February 2017, Volume 70, Issue 2, pp. 478-499.

4. Zaiwen Wen, Chao Yang, Xin Liu and Yin Zhang. Trace-Penalty Minimization for Large-scale Eigenspace Computation. Journal of Scientific Computing. March 2016, Volume 66, Issue 3, pp. 1175-1203.

5. Xin Liu, Zaiwen Wen and Yin Zhang. An Efficient Gauss-Newton Algorithm for Symmetric Low-Rank Product Matrix Approximations. SIAM Journal on Optimization. 25-3 (2015), pp. 1571-1608. http://dx.doi.org/10.1137/140971464.

6. Yuan Shen, Zaiwen Wen, and Yin Zhang. Augmented Lagrangian Alternating Direction Method for Matrix Separation Based on Low-Rank Factorization. Optimization Methods and Software. Vol. 29 (2), pp. 239-263. 2014.

7. Yin Zhang. Theory of Compressive Sensing via L1-Minimization: A Non-RIP Analysis and Extensions. Journal of the Operations Research Society of China. Vol. 1, Issue 1, pp. 79-105. 2013.

8. Junfeng Yang and Yin Zhang, Alternating direction algorithms for L1-problems in compressive sensing. SIAM Journal on scientific computing. 33(1), 250-278. 2011.

9. Yilun Wang, Junfeng Yang, Wotao Yin and Yin Zhang. A new alternating minimization algorithm for total variation image reconstruction. SIAM Journal on Imaging Sciences. 1(3), 248-272. 2008. 

10. Elaine T. Hale, Wotao Yin and Yin Zhang. Fixed-point continuation for L1-minimization: Methodology and convergence. SIAM Journal on Optimization. 19 (3), 1107-1130. 2008.

11. Renato Monteiro and Y Zhang. A unified analysis for a class of long-step primal-dual path-following interior-point algorithms for semidefinite programming. Mathematical Programming, Series A.81 (3), 281-299. 1998.

12. Yin Zhang. Solving large-scale linear programs by interior-point methods under the MATLAB environment. Optimization Methods and Software. 10 (1), 1-31. 1998.

13. Amer El-Bakry, Richard Tapia, T Tsuchiya and Yin Zhang. On the formulation and theory of the Newton interior-point method for nonlinear programming. Journal of Optimization Theory and Applications. 89 (3), 507-541. 1996.

14. Yin Zhang. On the convergence of a class of infeasible interior-point methods for the horizontal linear complementarity problem. SIAM Journal on Optimization. 4 (1), 208-227. 1994.

15. Yin Zhang, Richard Tapia, and John Dennis. On the superlinear and quadratic convergence of primal-dual interior point linear programming algorithms. SIAM Journal on Optimization. 2 (2), 304-324. 1992.