A GENERALIZED PROJECTION-SUCCESSIVE LINEAREQUATIONS ALGORITHM FOR NONLINEARLY EQUALITy AND INEQUALITY CONSTRAINED OPTIMIZATION AND ITS RATE OF CONVERGENCE

In this paper, a new superlinearly convergent algorithm is presented for optimization problems with general nonlineer equality and inequality Constraints, Comparing with other methods for these problems, the algorithm has two main advantages. First, it doesn‘t solve anyquadratic 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 algoritbh satisfy all inequity constraints and its step-length is computed by the straight line search,The algorithm is proved to possesa global and auperlinear convergence.

A generalized projection-successive linear equations algorithm for nonlinearly equality and inequality constrained optimization and its rate of 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. Supported by the Guangxi Youth Science Foundation and National Natural Science Foundation of China.