The joint bidiagonalization process with partial reorthogonalization. - Zhongxiao Jia; Haibo Li 007 (Numer. Algorithms, 2021)

Double precision is not necessary for LSQR for solving discrete linear ill-posed problems. - Haibo Li 007; Guangming Tan; Tong Zhao (CoRR, 2022)

The Joint Bidiagonalization Method for Large GSVD Computations in Finite Precision. - Zhongxiao Jia; Haibo Li 007 (SIAM J. Matrix Anal. Appl., 2023)

A Preconditioned Krylov Subspace Method for Linear Inverse Problems with General-Form Tikhonov Regularization. - Haibo Li 007 (SIAM J. Sci. Comput., 2024)

10-Million Atoms Simulation of First-Principle Package LS3DF. - Yujin Yan; Hai-Bo Li 007; Tong Zhao; Lin-Wang Wang; Lin Shi; Tao Liu; Guangming Tan; Weile Jia; Ninghui Sun (J. Comput. Sci. Technol., 2024)

Double Precision is not Necessary for LSQR for Solving Discrete Linear Ill-Posed Problems. - Haibo Li 007 (J. Sci. Comput., 2024)

The Joint Bidiagonalization of a Matrix Pair with Inaccurate Inner Iterations. - Haibo Li 007 (SIAM J. Matrix Anal. Appl., 2024)

Backward error analysis of the Lanczos bidiagonalization with reorthogonalization. - Haibo Li 007; Guangming Tan; Tong Zhao (J. Comput. Appl. Math., 2025)

Projected Newton Method for Large-Scale Bayesian Linear Inverse Problems. - Haibo Li 007 (SIAM J. Optim., 2025)

Characterizing GSVD by Singular Value Expansion of Linear Operators and Its Computation. - Haibo Li 007 (SIAM J. Matrix Anal. Appl., 2025)

A New Interpretation of the Weighted Pseudoinverse and Its Applications. - Haibo Li 007 (SIAM J. Matrix Anal. Appl., 2025)

Generalizing the SVD of a matrix under nonstandard inner product and its applications to linear ill-posed problems. - Hai-Bo Li 007 (Appl. Math. Comput., 2026)