Semidefinite inverse eigenvalue problems with prescribed entries and partial eigendata. - Teng-Teng Yao; Zheng-Jian Bai (J. Comput. Appl. Math., 2015)

A Riemannian Fletcher-Reeves Conjugate Gradient Method for Doubly Stochastic Inverse Eigenvalue Problems. - Teng-Teng Yao; Zheng-Jian Bai; Zhi Zhao; Wai-Ki Ching (SIAM J. Matrix Anal. Appl., 2016)

A Riemannian variant of the Fletcher-Reeves conjugate gradient method for stochastic inverse eigenvalue problems with partial eigendata. - Teng-Teng Yao; Zheng-Jian Bai; Zhi Zhao (Numer. Linear Algebra Appl., 2019)

A Riemannian derivative-free Polak-Ribiére-Polyak method for tangent vector field. - Teng-Teng Yao; Zhi Zhao; Zheng-Jian Bai; Xiao-Qing Jin (Numer. Algorithms, 2021)

The Riemannian two-step perturbed Gauss-Newton method for least squares inverse eigenvalue problems. - Zhi Zhao; Xiao-Qing Jin; Teng-Teng Yao (J. Comput. Appl. Math., 2022)

A generalized geometric spectral conjugate gradient algorithm for finding zero of a monotone tangent vector field on a constant curvature Hadamard manifold. - Zhi Zhao; Xiao-Qing Jin; Zheng-Jian Bai; Teng-Teng Yao (J. Comput. Appl. Math., 2023)

A Riemannian inexact Newton dogleg method for constructing a symmetric nonnegative matrix with prescribed spectrum. - Zhi Zhao; Teng-Teng Yao; Zheng-Jian Bai; Xiao-Qing Jin (Numer. Algorithms, 2023)

A projection algorithm for pseudomonotone vector fields with convex constraints on Hadamard manifolds. - Zhi Zhao; Qin Zeng; Yu-Nong Xu; Yaguan Qian; Teng-Teng Yao (Numer. Algorithms, 2023)

An efficient inertial projection-based algorithm for constrained nonlinear pseudo-monotone equations and its application to logistic regression problems. - Yong-Yan Yue; Teng-Teng Yao; Xiao-Qing Jin; Zhi Zhao (J. Comput. Appl. Math., 2025)