Proximal point algorithm with errors for generalized strongly nonlinear quasivariational inclusions

In this paper, a class of generalized strongly nonlinear quasivariational i nclusions are studied. By using the properties of the resolvent operator as sociated with a maximal monotone; mapping in Hilbert space, an existence th eorem of solutions for generalized strongly nonlinear quasivariational incl usion is established and a new proximal point algorithm with errors is sugg ested for finding approximate solutions which strongly converge to the exac t solution of the generalized strongly, nonlinear quasivariational inclusio n. As special cases, some known results in this field are also discussed.