An application of Newton-type iterative method for the approximate implementation of Lavrentiev regularization

Abstract

Motivated by the two-step directional Newton method considered by Argyros and Hilout (2010) for approximating a zero x* of a differentiable function F defined on a convex set D of a Hilbert space H, we consider a two-step Newton-Lavrentiev method (TSNLM) for obtaining an approximate solution to the nonlinear ill-posed operator equation F(x) = f , where F : D(F) ? X ? X is a nonlinear monotone operator defined on a real Hilbert space X. It is assumed that F(x) = f and that the only available data are f? with ¶f - f? ¶= ? ?. We prove that the TSNLM converges cubically to a solution of the equation F(x)+?(x-x<inf>o</inf>) = f? (such solution is an approximation of O x) where x0 is the initial guess. Under a general source condition on x<inf>0</inf>- x?, we derive order optimal error bounds by choosing the regularization parameter ? according to the balancing principle considered by Perverzev and Schock (2005). The computational results provided endorse the reliability and effectiveness of our method. © 2013 by Walter de Gruyter Berlin Boston.