Improvement Of Trust Region Method For Nonlinear Constrained Optimization

In this paper, we propose trust region method for nonlinear constrained optimization. The main content of the thesis is presented as follows:In chapter two, using L∞exact penalty function as objective function, on the base of new quasi-Newton equation, together with modified non-monotone line search skill, we designed new trust region method with non-monotone line search for constrained optimization. Global convergence and super linear convergent speed of the method are proved. Numerical experiments show that the new method is effective and suitable for large scale problems.In chapter three, based on trust region technique and quasi-Newton equation, by combining with Zhang H.C. non-monotone strategy, we present a new super-memory gradient method for unconstrained optimization problem. The global and convergence properties of the new method are proved. The numerical results show that the new methods are effective.