Research On Algorithm Of Seqential Quadratic Programming For Nonlinear Programming Problems

Nonlinear constrained optimization problems are the most generic subjects in mathematical programming. Recently, Many new methods are achieved to solve it,such as penalty function, feasible direction method, sequential quadratic programming.In this paper,we study the methods for solving inequality constrained optimization problems by a sequential quadratic programming algorithm.SQP method generates a sequence of quadratic programming (QP) subproblems. Our motivation for this work is originated from the applications of SQP in solving large-scale problems. we present and study an active set SQP algorithm for inequality constrained optimization. we establish QP subproblems on the active set of the original problem. A search direction is achieved by solving QP subproblems. we present general Exact penalty functions as line search function and obtain a better iterate. the global convergence of our algorithm is established under suitable conditions.