A TRUST REGION METHOD FOR NONLINEAR-PROGRAMMING BASED ON PRIMAL INTERIOR-POINT TECHNIQUES

This paper describes a new trust region method for solving large-scale optimization problems with nonlinear equality and inequality constrai nts. The new algorithm employs interior-point techniques from linear p rogramming, adapting them for more general nonlinear problems. A softw are implementation based entirely on sparse matrix methods is describe d. The software handles infeasible start points, identifies the active set of constraints at a solution, and can use second derivative infor mation to solve problems. Numerical results are reported for large and small problems, and a comparison is made with other large-scale optim ization codes.