Abstract
In this paper we address the stable numerical solution of nonlinear ill-posed systems by a trust-region method. We show that an appropriate choice of the trust-region radius gives rise to a procedure that has the potential to approach a solution of the unperturbed system. This regularizing property is shown theoretically and validated numerically.
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Behling, R., Fischer, A.: A unified local convergence analysis of inexact constrained Levenberg–Marquardt methods. Optim. Lett. 6, 927–940 (2012)
Bellavia, S., Cartis, C., Gould, N.I.M., Morini, B., Toint, Ph.L.: Convergence of a regularized euclidean residual algorithm for nonlinear least-squares. SIAM J. Numer. Anal. 48, 1–29 (2010)
Bellavia, S., Morini, B.: Strong local convergence properties of adaptive regularized methods for nonlinear least-squares. IMA J. Numer. Anal. 35, 947–968 (2015)
Cartis, C., Gould, N.I.M., Toint, Ph.L.: Trust-region and other regularizations of linear least-squares problems. BIT 49, 21–53 (2009)
Conn, A.R., Gould, N.I.M., Toint, Ph.L.: Trust-Region Methods. SMPS/SIAM Series on Optimization. SIAM, Philadelphia (2000)
de Sturler, E., Kilmer, M.: A regularized Gauss–Newton trust-region approach to imaging in diffuse optical tomography. SIAM J. Sci. Comput. 34, 3057–3086 (2011)
Fan, J.Y.: Convergence rate of the trust-region method for nonlinear equations under local error bound condition. Comput. Optim. Appl. 34, 215–227 (2005)
Fan, J.Y., Pan, J.Y.: A modified trust-region algorithm for nonlinear equations with updating rule for trust-region radius. Int. J. Comput. Math. 87, 3186–3195 (2010)
Fan, J.Y., Pan, J.Y.: An improved trust-region algorithm for nonlinear equations. Comput. Optim. Appl. 48, 59–70 (2011)
Gasparo, M.G., Papini, A., Pasquali, A.: A two-stage method for nonlinear inverse problems. J. Comput. Appl. Math. 198, 471–482 (2007)
Groetsch, C.V.: The Theory of Tikhonov Regularization for Fredholm Equations of the First Kind. Pitman Advanced Publishing Program, Boston (1984)
Hanke, M.: Regularizing Levenberg–Marquardt scheme, with applications to inverse groundwater filtration problems. Inverse Prob. 13, 79–95 (1997)
Hanke, M.: The regularizing Levenberg–Marquardt scheme is of optimal order. J. Integral Equ. Appl. 22, 259–283 (2010)
Henrici, P.: Elements of Numerical Analysis. Wiley, Chicester (1964)
Kaltenbacher, B.: Toward global convergence for strongly nonlinear ill-posed problems via a regularizing multilevel approach. Numer. Funct. Anal. Optim. 27, 637–665 (2006)
Kaltenbacher, B., Neubauer, A., Scherzer, O.: Iterative Regularization Methods for Nonlinear Ill-Posed Problems. Walter de Gruyter, Berlin (2008)
Kanzow, C., Yamashita, N., Fukushima, M.: Levenberg–Marquardt methods with strong local convergence properties for solving nonlinear equations with convex constraints. J. Comput. Appl. Math. 172, 375–397 (2004)
Levenberg, K.: A method for the solution of certain nonlinear problems in least-squares. Q. Appl. Math. 2, 164–168 (1944)
Macconi, M., Morini, B., Porcelli, M.: Trust-region quadratic methods for nonlinear systems of mixed equalities and inequalities. Appl. Numer. Math. 59(5), 859–876 (2009)
Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431–441 (1963)
Moré, J.J.: The Levenberg–Marquardt Algorithm: Implementation and Theory. In: Proceedings of 7th Biennial Conference, University of Dundee, Dundee, 1977, Lecture Notes in Mathematics, vol. 630, pp. 105–116. Springer, Berlin (1978)
Morozov, V.A.: On the solution of functional equations by the method of regularization. Sov. Math. Dokl. 7, 414–417 (1996)
Nocedal, J., Wright, S.J.: Numerical Optimization. Springer Series in Operations Research. Springer, New York (1999)
Toint, Ph.L.: Nonlinear stepsize control, trust regions and regularizations for unconstrained optimization. Optim. Methods Softw. 28, 82–95 (2013)
Vogel, C.R.: A constrained least squares regularization method for nonlinear ill-posed problems. SIAM J. Control Optim. 28, 34–49 (1990)
Vogel, C.R.: Computational Methods for Inverse Problems. Frontiers in Applied Mathematics. SIAM, Providence, RI (2002)
Wang, Y., Yuan, Y.: Convergence and regularity of trust region methods for nonlinear ill-posed problems. Inverse Prob. 21, 821–838 (2005)
Wang, Y., Yuan, Y.: On the regularity of trust region-CG algorithm for nonlinear ill-posed inverse problems with application to image deconvolution problem. Sci. China Ser. A 46, 312–325 (2003)
Zhang, J.L., Wang, Y.: A new trust region method for nonlinear equations. Math. Methods Oper. Res. 58, 283–298 (2003)
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Work partially supported by INdAM-GNCS, under the 2015 Project “Metodi di regolarizzazione per problemi di ottimizzazione e applicazioni”.
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Bellavia, S., Morini, B. & Riccietti, E. On an adaptive regularization for ill-posed nonlinear systems and its trust-region implementation. Comput Optim Appl 64, 1–30 (2016). https://doi.org/10.1007/s10589-015-9806-9
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DOI: https://doi.org/10.1007/s10589-015-9806-9