An extension of the Gauss-Newton algorithm is proposed to find local
minimizers of penalized nonlinear least squares problems, under generalized
Lipschitz assumptions. Convergence results of local type are obtained, as well
as an estimate of the radius of the convergence ball. Some applications for
solving constrained nonlinear equations are discussed and the numerical
performance of the method is assessed on some significant test problems
