A finite-dimensional regularized gradient method for solving irregular nonlinear operator equations

We construct and study a finite-dimensional iterative process of gradient type for the approximate solution of irregular nonlinear operator equations in a Hilbert space. Convergence properties of the process are studied in the presence of noise in input data. We propose a stopping criterion that ensures to obtain approximate solutions adequate to the level of errors in input data.