A projection method for general form linear least-squares problems

Golub, 1996 Pes, 2020, The minimal-norm Gauss-Newton method and some of its regularized variants, Electron. Trans. Numer. Anal., 53, 459, 10.1553/etna_vol53s459 Pes, 2022, A doubly relaxed minimal-norm Gauss–Newton method for underdetermined nonlinear least-squares problems, Appl. Numer. Math., 171, 233, 10.1016/j.apnum.2021.09.002 Baglama, 2007, Decomposition methods for large linear discrete ill-posed problems, J. Comput. Appl. Math., 198, 332, 10.1016/j.cam.2005.09.025 Morigi, 2006, A truncated projected SVD method for linear discrete ill-posed problems, Numer. Algorithms, 43, 197, 10.1007/s11075-006-9053-3 Hochstenbach, 2010, An iterative method for tikhonov regularization with a general linear regularization operator, J. Integral Equations Appl., 22, 465, 10.1216/JIE-2010-22-3-465 Park, 2018, Parameter determination for tikhonov regularization problems in general form, J. Comput. Appl. Math., 343, 12, 10.1016/j.cam.2018.04.049 Hansen, 2007, Regularization tools: version 4.0 for matlab 7.3, Numer. Algorithms, 46, 189, 10.1007/s11075-007-9136-9 Brezinski, 2008, Error estimates for linear systems with applications to regularization, Numer. Algorithms, 49, 85, 10.1007/s11075-008-9163-1 Brezinski, 2009, Error estimates for the regularization of least squares problems, Numer. Algorithms, 51, 61, 10.1007/s11075-008-9243-2 Reichel, 2013, Old and new parameter choice rules for discrete ill-posed problems, Numer. Algorithms, 63, 65, 10.1007/s11075-012-9612-8 Hansen, 1993, The use of the L-curve in the regularization of discrete ill–posed problems, SIAM J. Sci. Comput., 14, 1487, 10.1137/0914086 Hansen, 2007, An adaptive pruning algorithm for the discrete L-curve criterion, J. Comput. Appl. Math., 198, 483, 10.1016/j.cam.2005.09.026