A Quasi Newtonian Correction Algorithm for Weighted Total Least Squares Problem with Inequality Constraints

The errors-in-variables (EIV)model with inequality constraints is transformed into a standard nonlinear optimization program, which can be solved by existing optimization methods such as the active set method or sequential quadratic programming(SQP). Since weighted total least squares with inequality constraints (ICWTLS) is limited by the complexity of a Hessian matrix, which is the second partial derivative of objective function. In this paper, the Hessian matrix in SQP is replaced by an approximation based on Quasi-Newtonian method.The algorithm we propose can deal with the ICWTLS problem with a general weight matrix, and has the ability to handle large-scale problems. Eexamples illustrate that this new algorithm is efficient.