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 constraints. The new algorithm employs interior-point techniques from linear programming, adapting them for more general nonlinear problems. A software implementation based entirely on sparse matrix methods is described. The software handles infeasible start points, identifies the active set of constraints at a solution, and can use second derivative information to solve problems. Numerical results are reported for large and small problems, and a comparison is made with other large-scale optimization codes.

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