Truncated Gauss-Newton algorithms for Ill-conditioned nonlinear least squares problems.

We address numerical optimization algorithms for solving nonlinear least squares problems that lack well-defined solutions, in particular discrete parameter estimation problems. We present algorithms based on the Gauss-Newton method for both exactly and almost rank-deficient problems. Merit functions proposed have good global convergence properties. Numerical results that confirm local convergence results are presented. [ABSTRACT FROM AUTHOR]