Introduction

POSITION/TITLE

Professor

EDUCATION BACKGROUND

Ph.D. in Operations Research and Control Theory, Chinese Academy of Sciences, 1998

RESEARCH FIELD

Applied Mathematics, Mathematical Optimization, Sparse Signal Reconstruction, Compressed Sensing, Linear Inverse Problems and Numerical Analysis

BIOGRAPHY

Yun-Bin Zhao is currently a Professor (Adjunct) at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He is also a Professor of the Shenzhen Research Institute of Big Data (SRIBD).  He received his Ph.D. degree in operations research and control theory in 1998 from the Chinese Academy of Sciences. From 2003 to 2007, he was an Associate Professor in the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He joined to the University of Birmingham, UK in 2007 as a Lecturer and then Associate Professor since 2012. His working areas include applied mathematics, operations research, computational optimization, numerical linear algebra, compressed sensing, signal and image processing. His latest research is to develop efficient computational methods for sparse optimization problems and their applications to signal processing. He is the author of the book “Sparse Optimization Theory and Methods”, CRC Press/Taylor & Francis Group, 2018.

ACADEMIC PUBLICATIONS

A. Books

1.  Y.B. Zhao, “Sparse Optimization Theory and Methods, CRC Press, Boca Raton, FL, 2018.

B.  Journal articles

1. Y.B. Zhao and Z.-Q. Luo, Natural thresholding algorithms for signal recovery with sparsity, IEEE Open Journal of Signal Processing, 3 (2022), 417-431.

2. N. Meng, Y.B. Zhao, M. Kocvara and F. Sun, Partial gradient optimal thresholding algorithms for a class of sparse optimization problems, Journal of Global Optimization, 84 (2022), 393–413.

3. Y.B. Zhao and Z.-Q. Luo, Analysis of optimal thresholding algorithms for compressed sensing, Signal Processing, 187 (2021), 108148.

4. N. Meng and Y.B. Zhao, Newton-step-based hard thresholding algorithms for sparse signal recovery, IEEE Transactions on Signal Processing, 68 (2020), 6594-6606.

5. Y.B. Zhao, Optimal k-thresholding algorithms for sparse optimization problems, SIAM Journal on Optimization, 30 (2020), no. 1, 31-55

6. Y.B. Zhao, H. Jiang and Z.-Q. Luo, Weak stability of $\ell$-minimization methods in sparse data reconstruction, Mathematics of Operations Research, 44 (2019), no.1, 173–195.

7. Y.B. Zhao, Z.Q. Luo, Constructing new weighted L1-algorithms for the sparsest points of polyhedral sets, Mathematics of Operations Research, 42 (2017), no.1, 57--76.

8. Y.B. Zhao and M. Kocvara, A new computational method for the sparsest solutions to systems of linear equations, SIAM Journal on Optimization, 25 (2015), no.2, 1110-1134

9. Y.B. Zhao, Equivalence and strong equivalence of sparsest and least l1-norm nonnegative solutions to linear systems and their application, Journal of the Operations Research Society of China, 2 (2014), 171-193.

10. Y.B. Zhao, RSP-based analysis for sparsest and least l1-norms solutions to underdetermined linear systems, IEEE Transactions on Signal Processing, 61 (2013), no.22, 5777-5788

11. Y.B. Zhao and D. Li, Reweighted l1-minimization for sparse solutions to underdetermined linear systems, SIAM Journal on Optimization, 22 (2012), no. 3, 1065-1088.

12 Y.B. Zhao, An approximation theory of matrix rank minimization and its application to quadratic equations, Linear Algebra and Its Applications, 437 (2012), no. 1, 77--93.

13. Y.B. Zhao, Convexity condition and Legendre-Fenchel transform of the product of finitely many quadratic forms, Applied Mathematics and Optimization, 62 (2010), no.3, 411--434.

14. Y.B. Zhao, The Legendre-Fenchel conjugate of the product of two positive-definite quadratic forms, SIAM Journal on Matrix Analysis and Applications, 31 (2010). no. 4, 1792-1811.

15. I. Averbakh and Y.B. Zhao, Explicit reformulation for robust mathematical optimization with general uncertainty sets, SIAM Journal on Optimization, 18 (2008), no. 4, 1436--1466.

16. Y.B. Zhao, S.C. Fang and D. Li, Constructing generalized mean functions using convex functions with regularity, SIAM Journal On Optimization, 17 (2006), no. 1, 37-51.

17. J. Peng, T. Terlaky and Y.B. Zhao, A predictor-corrector algorithm for linear optimization based on a specific self-regular proximity function. SIAM Journal on Optimization, 15 (2005), no. 4, 1105--1127.

18. Y.B. Zhao and D. Li, A globally and locally superlinearly convergent non-interior-point algorithm for P0 LCPs, SIAM Journal on Optimization, 13 (2003), no. 4, 1195-1221.

19. Y.B. Zhao and D. Li, Locating the least 2-norm solution of linear programs via a path-following method, SIAM Journal on Optimization, 12 (2002), no. 4, 893-912.

20. Y.B. Zhao and D. Li, Existence and limiting behavior of a non-interior-point trajectory for nonlinear complementarity problems without strict feasibility condition. SIAM Journal on Control and Optimization, 40 (2001), no. 3, 898-924.

21. Y.B. Zhao and D. Li, On a new homotopy continuation trajectory for nonlinear complementary problems. Mathematics of Operations Research, 26 (2001), no. 1, 119--146

22. Y.B. Zhao and D. Li, Monotonicity of fixed point and normal mappings associated with variational inequality and its application. SIAM Journal on Optimization, 11 (2001), no. 4, 962--973.

23. Y.B. Zhao and G. Isac, Properties of a multi-valued mapping associated with some non-monotone complementarity problems. SIAM Journal on Control and Optimization, 39 (2000), no. 2, 571-593.