Liaoyuan Zeng

Associate Professor

School of Mathematical Sciences, Zhejiang University of Technology

Email: zengly ‘at’ zjut.edu.cn

About me

I received a B.S. in mathematics from Hunan University in 2013. I got my PhD degree in 2018, under the supervision of Professor Yu-Hong Dai in Academy of Mathematics and Systems Science of the Chinese Academy of Sciences. I was a postdoctoral fellow at The Hong Kong Polytechnic University, under the mentorship of Professor Xiaojun Chen during October 2018 to October 2019. From January 2020 to September 2022, I worked as a postdoctoral fellow with Professor Ting Kei Pong at The Hong Kong Polytechnic University. I joined Zhejiang University of Technology on September, 2022.

Research Interest

Nonlinear optimization and its applications: first-order methods for large-scale (convex or nonconvex) problems.

Publications

  1. Peiran Yu, Liaoyuan Zeng and Ting Kei Pong. Convergence analysis for a variant of manifold proximal point algorithm based on Kurdyka-Łojasiewicz property. Pac. J. Optim. 21, 2025, pp. 761-785.

  2. Liaoyuan Zeng, Yongle Zhang, Guoyin Li, Ting Kei Pong and Xiaozhou Wang. Frank-Wolfe-type methods for a class of nonconvex inequality-constrained problems. Math. Prog. 208, 2024, pp. 717-761.  [code]

  3. Liaoyuan Zeng and Ting Kei Pong. \(\rho\)-regularization subproblems: Strong duality and an eigensolver-based algorithm. Comput. Optim. Appl. 81, 2022, pp. 337-368.  [code]

  4. Liaoyuan Zeng, Peiran Yu and Ting Kei Pong. Analysis and algorithms for some compressed sensing models based on L1/L2 minimization. SIAM J. Optim. 31, 2021, pp. 1576-1603.  [code]

  5. Chen Chen, Ting Kei Pong, Lulin Tan and Liaoyuan Zeng. A difference-of-convex approach for split feasibility with applications to matrix factorizations and outlier detection. J. Global Optim. 78, 2020, pp. 107-136. See the ArXiv version for a fix of minor errors in Lemma 4 and Proposition 5.  [Codes available at Ting Kei Pong's homepage.]

  6. Liaoyuan Zeng, Yu-Hong Dai and Ya-Kui Huang. Convergence rate of gradient descent method for multi-objective optimization. J. Comp. Math. 37, 2019, pp. 689-703.

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