QP-FREE, TRUNCATED HYBRID METHODS FOR LARGE-SCALE NONLINEAR CONSTRAINED OPTIMIZATION

摘要

In this paper, a truncated hybrid method is proposed and developed for solv- ing sparse large-scale nonlinear programming problems. In the hybrid method, a symmetric system of linear equations, instead of the usual quadratic program- ming subproblems, is solved at iterative process. In order to ensure the global convergence, a method of multiplier is inserted in iterative process. Atruncated solution is determined for the system of linear equations and the unconstrained subproblem are solved by the limited memory BFGS algorithm such that the hy- brid algorithm is suitable to the large-scale problem.