Regularized total least squares based on quadratic eigenvalue problem solvers

This paper presents a new computational approach for solving the Regularized Total Least Squares problem. The problem is formulated by adding a quadratic constraint to the Total Least Square minimization problem. Starting from the fact that a quadratically constrained Least Squares problem can be solved via a quadratic eigenvalue problem, an iterative procedure for solving the regularized Total Least Squares problem based on quadratic eigenvalue problems is presented. Discrete ill-posed problems are used as simulation examples in order to numerically validate the method.