摘要
In this paper, a new superlinearly convergent algorithm is presented for optimization problems with general nonlinear equality and inequality constraints. Comparing with other methods for these problems, the algorithm has two main advantages. First, it doesn’t solve any quadratic programming (QP), and its search directions are determined by the generalized projection technique and the solutions of two systems of linear equations. Second, the sequential points generated by the algorithm satisfy all inequality constraints and its step length is computed by the straight line search. The algorithm is proved to possess global and superlinear convergence.
In this paper, a new superlinearly convergent algorithm is presented for optimization problems with general nonlinear equality and inequality constraints. Comparing with other methods for these problems, the algorithm has two main advantages. First, it doesn’t solve any quadratic programming (QP), and its search directions are determined by the generalized projection technique and the solutions of two systems of linear equations. Second, the sequential points generated by the algorithm satisfy all inequality constraints and its step length is computed by the straight line search. The algorithm is proved to possess global and superlinear convergence.
