An algorithm for solving sparse Nonlinear Least Squares problems

We introduce a new method for solving Nonlinear Least Squares problems when the Jacobian matrix of the system is large and sparse. The main features of the new method are the following:

1. The Gauss-Newton equation is “partially” solved at each iteration using a preconditioned Conjugate Gradient algorithm.
2. The new point is obtained using a two-dimensional trust region scheme, similar to the one introduced by Bulteau and Vial.

We prove global and local convergence results and we present some numerical experiments.