In this paper,we extend the ST decomposition to the generalized saddle point problem and present three block triangular preconditioners.

Then we concentrate on the construction of the discrete space of Lagrange multiplier, the space of mortar elements, and the conjugated gradient method for the related saddle point problem.

To improve the convergence rate of the krylov subspace methods to solve saddle point problems,the block overrelaxation-type preconditioner is presented based on the block overrelaxation-type matrix splitting of the coefficient matrix.

In this paper,we present a convergent result of the iterative solution methods for a class of generalized saddle point problem,which lowers the condition of the recent results.

In this paper,we prove that in the sense of Baire category,most of the saddle point problems are generic.

Solving saddle point problems for large and sparse systems is applied in many fields, such as constrained optimization,least square problems,image management and so on.

Super efficient point in vector optimization problems with set-valued maps characterized by generalized saddle point;

By using the properties of generalized saddle points and a separation theorem,a property of generalized saddle points is proved with set separation.

This paper establishes one kind of constructions for vector Lagrangian functionals in a class of multiobjective fractional optimal control problems, called generalized Lagrangian functionals, and the relationship between the weak efficiency and generalized weak saddle points of such a kind of generalized Lagrangian functionals is discussed.