Numerical Method Of Large Sparse Saddle Point Problems

Saddle point problems are widely involved in many areas of scientific research and engineering computations, such as fluid dynamics, elasticity, electromagnetics, constraint optimization problems and least square problems. Because these problems have so wide application source and value, it is of great interest to develop fast and efficient methods.In this thesis, we discuss numerical method of the large sparse saddle point problems. We generalize the parameterized inexact Uzawa methods to the generalized saddle point problems, obtaining a new method denoting GPIU theme. By choosing different matrix splitting, we can get some existing and new methods, the convergence conditions of the GPIU method are also analyzed. Numerical experiments show that this new method is feasible and effective.