[1] The convergence of generalized Lanczos methods for large unsymmetric eigenproblems, SIAM Journal on Matrix Analysis and Applications, 16 (3) (1995), 843—862.
[2] A block incomplete orthogonalization method for large nonsymmetric eigenproblems, BIT, 34 (4) (1995), 516—539.
[3] On IOM(q): the incomplete orthogonalization method for large unsymmetric linear systems, Numerical Linear Algebra with Applications, 3 (6) (1996), 491—512.
[4] Refined iterative algorithms based on Arnoldi's process for large unsymmetric eigenproblems, Linear Algebra and Its Applications,259 (1997), 1—23.
[5] A refined iterative algorithm based on the block Arnoldi process for large unsymmetric eigenproblems, Linear Algebra and Its Applications, 270 (1998), 171—189.
[6] Generalized block Lanczos methods for large unsymmetric eigenproblems, Numerische Mathematik, 80 (2) (1998), 239—266.
[7] On IGMRES: an incomplete generalized minimal residual method for large unsymmetric linear systems, Science in China (Series A), 41 (12)(1998), 1178—1188.
[8] A variation on the block Arnoldi method for large unsymmetric eigenproblems, Acta Mathematica Applicatae Sinica, 14 (4) (1998), 425—432.
[9] The convergence of Krylov subspace methods for large unsymmetric linear systems, Acta Mathematica Sinica-New Series, 14 (4) (1998), 507—518.
[10] Polynomial characterizations of the approximate eigenvectors by the refined Arnoldi method and an implicitly restarted refined Arnoldi algorithm, Linear Algebra and Its Applications, 287 (1999), 191—214.
[11] Composite orthogonal projection methods for large matrix eigenproblems, Science in China (Series A), 42 (6) (1999), 577—585.
[12] Arnoldi type algorithms for large unsymmetric multiple eigenvalue problems, Journal of Computational Mathematics，17 (3) (1999), 257—274.
[13] A refined subspace iteration algorithm for large sparse eigenproblems, Applied Numerical Mathematics，32(1)(2000), 35—52.
[14] Some recursions on Arnoldi's method and IOM for large non-Hermitian linear systems, Computers and Mathematics with Applications, 39 (3/4) (2000), 125—129.
[15] Jia Z. and Elsner L., Improving eigenvectors in Arnoldi's method, Journal of Computational Mathematics, 18 (3) (2000), 365—376.
[16] Jia Z. and Stewart G.W., An analysis of the Rayleigh—Ritz method for approximating eigenspaces, Mathematics of Computation, 70 (234) (2001), 637—647.
[17] On residuals of refined projection methods for large matrix eigenproblems, Computers and Mathematics with Applications, 41 (7/8) (2001), 813—820.
[18] The refined harmonic Arnoldi method and an implicitly restarted refined algorithm for computing interior eigenpairs of large matrices, Applied Numerical Mathematics, 42 (4) (2002), 489—512.
[19] Chen G. and Jia Z. A reverse order implicit Q-theorem and the Arnoldi process, Journal of Computational Mathematics, 20 (5) (2002), 519—524.
[20] Jia Z. and Zhang Y., A refined invert-and-shift Arnoldi algorithm for large generalized unsymmetric eigenproblems, Computers and Mathematics with Applications, 44 (8/9) (2002), 1117—1127.
[21] Jia Z. and Niu D., An implicitly restarted refined bidiagonalization Lanczos method for computing a partial singular value decomposition, SIAM Journal on Matrix Analysis and Applications, 25(1) (2003), 246—265.
[22] Chen G and Jia Z, Theoretical and numerical comparisons of GMRES and WZ-GMRES, Computers and Mathematics with Applications, 47 (8/9) (2004), 1335—1350.
[23] Chen G and Jia Z., An analogue of the results of Saad and Stewart for harmonic Ritz vectors, Journal of Computational and Applied Mathematics,167 (2004), 493—498.
[24] Some theoretical comparisons of refined Ritz vectors and Ritz vectors, Science in China, Series A, 47 (Suppl.) (2004), 222—233.
[25] Feng S. and Jia Z., A refined Jacobi-Davidson method and its correction equation, Computers and Mathematics with Applications, 49 (2/3) (2005), 417—427.
[26] The convergence of harmonic Ritz values, harmonic Ritz vectors and refined harmonic Ritz vectors, Mathematics of Computation, 74 (251) (2005), 1441—1456.
[27] Chen G. and Jia Z., A refined harmonic Rayleigh-Ritz procedure and an explicitly restarted refined harmonic Arnoldi algorithm, Mathematical and Computer Modelling, 41 (2005), 615—627.
[28] Using cross-product matrices to compute the SVD, Numerical Algorithms, 42 (1) (2006), 31—61.
[29] Jia Z. and Sun Y., A QR decomposition based solver for the least squares problem from the minimal residual method, Journal of Computational Mathematics, 25 (5) (2007), 531—542.
[30] 贾仲孝，王震，非精确Rayleigh商迭代和非精确的简化Jacobi—Davidson方法的收敛性分析，中国科学, A辑，38 (4) (2008): 365--376. Jia Z. and Wang Z., A convergence analysis of the inexact Rayleigh quotient iteration and simplified Jacobi—Davidson method for the large Hermitian matrix eigenproblem, Science in China Series A, 51 (12) (2008), 2205--2216.
[31] Jia Z. and Zhu B., A power sparse approximate inverse preconditioning procedure for large linear systems, Numerical Linear Algebra with Applications, 16 (4) (2009), 259—299.
[32] Applications of the Conjugate Gradient (CG) method in optimal surface parameterizations, International Journal of Computer Mathematics, 87 (5) (2010), 1032—1039.
[33] Jia Z. and Niu D., A refined harmonic Lanczos bidiagonalization method and an implicitly restarted algorithm for computing the smallest singular triplets of large matrices, SIAM Journal on Scientific Computing, 32 (2) (2010), 714—744.
[34] Some properties of LSQR for large sparse linear least squares problems, Journal of Systems Science and Complexity, 23 (4) (2010), 815—821.
[35] Duan C. and Jia Z., A global harmonic Arnoldi method for large non-Hermitian eigenproblems with an application to multiple eigenvalue problems, Journal of Computational and Applied Mathematics, 234 (2010), 845—860.
[36] Chu, E K.-W., Fan H.Y., Jia Z., Li T. and Lin W.-W, The Rayleigh—Ritz method, refinement and Arnoldi process for periodic matrix pairs, Journal of Computational and Applied Mathematics, 235 (2011), 2626—2639.
[37] Duan D and Jia Z., A global Arnoldi method for large non-Hermitian eigenproblems with special applications to multiple eigenproblems, Taiwanese Journal of Mathematics, 15 (4) (2011), 1497—1525.
[38] Li B. and Jia Z., Some results on condition numbers of the scaled total least squares problems, Linear Algebra and Its Applications, 435 (3) (2011), 674—686.
[39] On convergence of the inexact Rayleigh quotient iteration with MINRES, Journal of Computational and Applied Mathematics, 236 (2012), 4276—4295.
[40] Jia Z. and Sun Y., SHIRRA: A refined variant of SHIRA for the Skew-Hamiltonian/Hamiltonian (SHH) pencil eigenvalue problem, Taiwanese Journal of Mathematics, 17 (1) (2013), 259—274. (SCI)
[41] On convergence of the inexact Rayleigh quotient iteration with the Lanczos method used for solving linear systems, Science China Mathematics, 56 (10) (2013), 2145—2160.
[42] Jia Z. and Li B., On the condition number of the total least squares problem, Numerische Mathematik, 125 (1) (2013), 61—87.
[43] Jia Z. and Zhang Q., An approach to making SPAI and PSAI preconditioning effective for large irregular sparse linear systems, SIAM Journal on Scientific Computing, 35 (4) (2013), A1903—A1927.
[44] Huang T-M, Jia Z. and Lin W-W., On the convergence of Ritz pairs and refined Ritz vectors for quadratic eigenvalue problems, BIT Numerical Mathematics, 53 (4) (2013), 941—958.
[45] Jia Z. and Zhang Q., Robust dropping criteria for F-norm minimization based sparse approximate inverse preconditioning, BIT Numerical Mathematics, 53 (4) (2013), 959—985.
[46] Jia Z. and Li C., Inner iterations in the shift-invert residual Arnoldi method and the Jacobi—Davidson method, Science China Mathematics, 57 (8) (2014), 1733—1752.
[47] Jia Z. and Li C., Harmonic and refined harmonic shift-invert residual Arnoldi and Jacobi—Davidson methods for interior eigenvalue problems, Journal of Computational and Applied Mathematics, 282 (2015), 83--97.
[48] Jia Z. and Sun Y., Implicitly restarted generalized second-order Arnoldi type algorithms for the quadratic eigenvalue problem, Taiwanese Journal of Mathematics, 19 (1) (2015), 1—30.
[49] Jia Z. and Lv H., A posteriori error estimates of Krylov subspace approximations to matrix functions, Numerical Algorithms, 69 (1) (2015), 1—28.
[50] Jia Z., Lin W.-W and Liu C.-S. A positivity preserving inexact Noda iteration for computing the smallest eigenpair of a large irreducible M-matrix, Numerische Mathematik, 130 (4) (2015), 645—679.
[51] Huang Y. and Jia Z., Some results on regularization of LSQR for large-scale discrete ill-posed problems, Science China Mathematics, 60 (4) (2017), 701—718.
[52] Jia Z. and Kang WJ., A residual based sparse approximate inverse preconditioning procedure for large sparse linear systems, Numerical Linear Algebra with Applications, 24 (2) (2017), 1—13.
[53] Huang Y. and Jia Z., On regularizing effects of MINRES and MR-II for large-scale symmetric discrete ill-posed problems, Journal of Computational and Applied Mathematics, 320 (2017), 145—163.
[54] Jia Z. and Yang Y., Modified truncated randomized singular value decomposition (MTRSVD) algorithms for large scale discrete ill-posed problems with general-form regularization, Inverse Problems, 34 (2018), article no. 055013 (28pp).
[55] Jia Z. and Kang WJ., A transformation approach that makes SPAI, PSAI and RSAI procedures efficient for large double irregular nonsymmetric sparse linear systems, Journal of Computational and Applied Mathematics, 384 (2019), 200—213.
[56] Huang J. and Jia Z., On inner iterations of Jacobi-Davidson type methods for large SVD computations, SIAM Journal on Scientific Computing, 41 (3) (2019), A1574—A1603.
[57] Approximation accuracy of the Krylov subspaces for linear discrete ill-posed problems, Journal of Computational and Applied Mathematics, 374 (2020), article no. 112786.
[58] The low rank approximations and Ritz values in LSQR for linear discrete ill-posed problems, Inverse Problems, 36 (4) (2020), article no. 045013 (32pp).
[59] Regularization properties of the Krylov iterative solvers CGME and LSMR for linear discrete ill-posed problems with an application to truncated randomized SVDs, Numerical Algorithms, 85 (4) (2020), 1281—1310.
[60] Regularization properties of LSQR for linear discrete ill-posed problems in the multiple singular value and best, near best and general low rank approximations, Inverse Problems, 36 (8) (2020), article no. 085009 (38pp).
[61] Jia Z. and Yang Y., A joint bidiagonalization based algorithm for large scale general-form Tikhonov regularization, Applied Numerical Mathematics, 157 (2020), 159--177.
[62] Huang J. and Jia Z., On choices of formulations of computing the generalized singular value decomposition of a matrix pair, Numerical Algorithms, 87 (2021), 689—718.
[63] Jia Z. and Lai. F., A convergence analysis on the iterative trace ratio algorithm and its refinements,CSIAM Transactions on Applied Mathematics, 2 (2) (2121), 297–312.
[64] Jia Z. and Wang F., The convergence of the generalized Lanczos trust-region method for the trust-region subproblem, SIAM Journal on Optimization, 31 (1) (2021), 887—914.
[65] Jia Z. and Li H., The joint bidiagonalization process with partial reorthogonalization, Numerical Algorithms, 88 (2021), 965—992.
[66] Theoretical and computable optimal subspace expansions for matrix eigenvalue problems, SIAM Journal on Matrix Analysis and Applications, 43 (2) (2022), 584—604.
[67] Huang J. and Jia Z., Two harmonic Jacobi--Davidson methods for computing a partial generalized singular value decomposition of a large matrix pair, Journal of Scientific Computing, 93 (2022), article no. 41. (29pp).
[68] Huang J. and Jia Z., A cross-product free Jacobi—Davidson type method for computing a partial generalized singular value decomposition of a large matrix pair, Journal of Scientific Computing, 94 (2023), article no. 3 (32pp).
[69] Jia Z. and Li H., The joint bidiagonalization method for large GSVD computations in finite precision, SIAM Journal on Matrix Analysis and Applications, 44 (1) (2023), 382—407.
[70] Jia Z. and Zhang K., A FEAST SVDsolver for the computation of singular value decompositions of large matrices based on the Chebyshev—Jackson series expansion, Journal of Scientific Computing, 97 (2023), article no. 21 (36pp).
[71] Jia Z. and Zhang K., An augmented matrix-based CJ-FEAST SVDsolver for computing a partial singular value decomposition with the singular values in a given interval, SIAM Journal on Matrix Analysis and Applications, 45 (1) (2023), 24—58.
[72] Huang J. and Jia Z., A skew-symmetric Lanczos bidiagonalization method for computing several extremal eigenpairs of a large skew-symmetric matrix, SIAM Journal on Matrix Analysis and Applications, 45 (2) (2024), 1114—1147.
[73] Huang J. and Jia Z., Refined and refined harmonic Jacobi–Davidson methods for computing several GSVD components of a large regular matrix pair, Numerical Algorithms, 99 (2025), 895—920.
[74] Jia Z. and Zheng QQ., An analysis of the Rayleigh-Ritz and refined Rayleigh-Ritz methods for regular nonlinear eigenvalue problems, SIAM Journal on Matrix Analysis and Applications,46 (1) (2025), 676—701.
[75] Jia Z. and Zhang K., A CJ-FEAST GSVDsolver for computing a partial GSVD of a large matrix pair with the generalized singular values in a given interval, Numerische Mathematik, 157 (2025): 897—949.
