1. Alam, S.M.S., Natarajan, B., Pahwa, A.: Distribution grid state estimation from compressed measurements. IEEE Trans. Smart Grid 5, 1631–1642 (2014)
2. An, H.-B., Bai, Z.-Z.: NGLM: a globally convergent Newton-GMRES method (in Chinese). Math. Numer. Sin. 27, 151–174 (2005)
3. An, H.-B., Bai, Z.-Z.: A globally convergent Newton-GMRES method for large sparse systems of nonlinear equations. Appl. Numer. Math. 57, 235–252 (2007)
4. Bai, Z.-Z., An, H.-B.: On efficient variants and global convergence of the Newton-GMRES method (in Chinese). J. Numer. Methods Comput. Appl. 26, 291–300 (2005)
5. Bai, Z.-Z., Guo, X.-P.: On Newton-HSS methods for systems of nonlinear equations with positive-definite Jacobian matrices. J. Comput. Math. 28, 235–260 (2010)
6. Bai, Z.-Z., Wang, L.: On convergence rates of Kaczmarz-type methods with different selection rules of working rows. Appl. Numer. Math. 186, 289–319 (2023)
7. Bai, Z.-Z., Wu, W.-T.: On greedy randomized Kaczmarz method for solving large sparse linear systems. SIAM J. Sci. Comput. 40, A592–A606 (2018)
8. Bai, Z.-Z., Wu, W.-T.: On convergence rate of the randomized Kaczmarz method. Linear Algebra Appl. 553, 252–269 (2018)
9. Bao, J.-F., Yu, C.K.W., Wang, J.-H., Hu, Y.-H., Yao, J.-C.: Modified inexact Levenberg-Marquardt methods for solving nonlinear least squares problems. Comput. Optim. Appl. 74, 547–582 (2019)
10. Bellavia, S., Morini, B.: A globally convergent Newton-GMRES subspace method for systems of nonlinear equations. SIAM J. Sci. Comput. 23, 940–960 (2001)
11. Ben-Israel, A.: A Newton-Raphson method for the solution of systems of equations. J. Math. Anal. Appl. 15, 243–252 (1966)
12. Brown, K.M.: A quadratically convergent Newton-like method based upon Gaussian elimination. SIAM J. Numer. Anal. 6, 560–569 (1969)
13. Byrne, C.: A unified treatment of some iterative algorithms in signal processing and image reconstruction. Inverse Probl. 20, 103–120 (2004)
14. Censor, Y.: Row-action methods for huge and sparse systems and their applications. SIAM Rev. 23, 444–466 (1981)
15. Censor, Y.: Parallel application of block-iterative methods in medical imaging and radiation therapy. Math. Progr. 42, 307–325 (1988)
16. Chen, J.-Q., Huang, Z.-D.: On a fast deterministic block Kaczmarz method for solving large-scale linear systems. Numer. Algorithms 89, 1007–1029 (2022)
17. Chen, Q.-P., Hao, W.-R.: Randomized Newton’s method for solving differential equations based on the neural network discretization. J. Sci. Comput. 92, 49 (2022)
18. Chen, X.-J., Womersley, R.S.: Existence of solutions to systems of underdetermined equations and spherical designs. SIAM J. Numer. Anal. 44, 2326–2341 (2006)
19. Du, K., Gao, H.: A new theoretical estimate for the convergence rate of the maximal weighted residual Kaczmarz algorithm. Numer. Math. Theory Methods Appl. 12, 627–639 (2019)
20. Eggermont, P.P.B., Herman, G.T., Lent, A.: Iterative algorithms for large partitioned linear systems, with applications to image reconstruction. Linear Algebra Appl. 40, 37–67 (1981)
21. Elble, J.M., Sahinidis, N.V., Vouzis, P.: GPU computing with Kaczmarz’s and other iterative algorithms for linear systems. Parall. Comput. 36, 215–231 (2010)
22. Gordon, R., Bender, R., Herman, G.T.: Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. J. Theor. Biol. 29, 471–481 (1970)
23. Gower, R.M., Richtárik, P.: Randomized iterative methods for linear systems. SIAM J. Matrix Anal. Appl. 36, 1660–1690 (2015)
24. Haltmeier, M., Leitao, A., Scherzer, O.: Kaczmarz methods for regularizing nonlinear ill-posed equations I: convergence analysis. Inverse Probl. Imaging 1, 289–298 (2007)
25. Hanke, M., Neubauer, A., Scherzer, O.: A convergence analysis of the Landweber iteration for nonlinear ill-posed problems. Numer. Math. 72, 21–37 (1995)
26. Herman, G.T., Meyer, L.B.: Algebraic reconstruction techniques can be made computationally efficient. IEEE Trans. Med. Imaging 12, 600–609 (1993)
27. Hounsfield, G.N.: Computerized transverse axial scanning (tomography). Part I. Description of system (Reprinted from British-Journal-of-Radiology. 46, 1016–1022, 1973). Br. J. Radiol. 68, 166–172 (1995)
28. Kaczmarz, S.: Angenäherte auflösung von systemen linearer gleichungen. Bull. Int. Acad. Polon. Sci. Lett. A 35, 355–357 (1937)
29. Kak, A.C., Slaney, M.: Principles of Computerized Tomographic Imaging. IEEE Press, New York (1988)
30. Kaltenbacher, B., Neubauer, A., Scherzer, O.: Iterative Regularization Methods for Nonlinear Ill-Posed Problems. Walter de Gruyter, Berlin (2008)
31. Kelley, C.T.: Solving Nonlinear Equations with Newton’s Method. SIAM, Philadelphia (2003)
32. Kuegler, P.: A sparse update method for solving underdetermined systems of nonlinear equations applied to the manipulation of biological signaling pathways. SIAM J. Appl. Math. 72, 982–1001 (2012)
33. Li, L., Li, W.-G., Xing, L.-L., Bao, W.-D.: Nonlinear greedy relaxed randomized Kaczmarz method. Results Appl. Math. 16, 100340 (2022)
34. Liu, C.-W.: An acceleration scheme for row projection methods. J. Comput. Appl. Math. 57, 363–391 (1995)
35. Martínez, J.M.: The projection method for solving nonlinear systems of equations under the “most violated constraint” control. Comput. Math. Appl. 11, 987–993 (1985)
36. McCormick, S.F.: The methods of Kaczmarz and row orthogonalization for solving linear equations and least squares problems in Hilbert space. Indiana Univ. Math. J. 26, 1137–1150 (1977)
37. Meyn, K.H.: Solution of underdetermined nonlinear equations by stationary iteration methods. Numer. Math. 42, 161–172 (1983)
38. Moré, J.J., Garbow, B.S., Hillstrom, K.E.: Testing unconstrained optimization software. ACM Trans. Math. Softw. 7, 17–41 (1981)
39. Needell, D., Srebro, N., Ward, R.: Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm. Math. Program. 155, 549–573 (2016)
40. Needell, D., Tropp, J.A.: Paved with good intentions: analysis of a randomized block Kaczmarz method. Linear Algebra Appl. 441, 199–221 (2014)
41. Niu, Y.-Q., Zheng, B.: A greedy block Kaczmarz algorithm for solving large-scale linear systems. Appl. Math. Lett. 104, 106294 (2020)
42. Ortega, J.M., Rheinboldt, W.C.: Iterative Solution of Nonlinear Equations in Several Variables. Academic Press, New York (1970)
43. Pasqualetti, F., Carli, R., Bullo, F.: Distributed estimation via iterative projections with application to power network monitoring. Autom. J. IFAC 48, 747–758 (2012)
44. Romero, L.A., Mason, J.: Geolocation using TOA, FOA, and altitude information at singular geometries. IEEE Trans. Aerosp. Electron. Syst. 51, 1069–1078 (2015)
45. Shao, C.-P.: A deterministic Kaczmarz algorithm for solving linear systems. SIAM J. Matrix Anal. Appl. 44, 212–239 (2023)
46. Strohmer, T., Vershynin, R.: A randomized Kaczmarz algorithm with exponential convergence. J. Fourier Anal. Appl. 15, 262–278 (2009)
47. Tompkins, C.: Projection methods in calculation. In: Proceedings of the Second Symposium in Linear Programming, Washington, D.C., 1955, pp. 425–448. National Bureau of Standards, Washington, DC (1955)
48. Wang, Q.-F., Li, W.-G., Bao, W.-D., Gao, X.-Q.: Nonlinear Kaczmarz algorithms and their convergence. J. Comput. Appl. Math. 399, 113720 (2022)
49. Ye, W.-N., Zhang, M., Zhu, Y., Wang, L.-J., Hu, J.-C., Li, X., Hu, C.-X.: Real-time displacement calculation and offline geometric calibration of the grating interferometer system for ultra-precision wafer stage measurement. Precis. Eng. 60, 413–420 (2019)
50. Yuan, R., Lazaric, A., Gower, R.M.: Sketched Newton-Raphson. SIAM J. Optim. 32, 1555–1583 (2022)
51. Zhang, F.-Y., Bao, W.-D., Li, W.-G., Wang, Q.: On sampling Kaczmarz-Motzkin methods for solving large-scale nonlinear systems. Comput. Appl. Math. 42, 126 (2023)
52. Zhang, J.-H., Wang, Y.-Q., Zhao, J.: On maximum residual nonlinear Kaczmarz-type algorithms for large nonlinear systems of equations. J. Comput. Appl. Math. 425, 115065 (2023)