In this paper we propose iterative solvers for portfolio optimization in a two dimensional domain. To put the modeled equations into practice we provide Jacobi and Gauss-Seidal algorithms. In order to improve the efficiency of portfolio optimization iterative solvers we study the convergence rate and introduce successive over relaxation scheme to the developed algorithms. Further to overcome the domain bias of this relaxation scheme we propose a symmetric successive relaxation model. This is demonstrated through a Chebyshev acceleration technique. We conclude by stating that iterative solvers are more superior and consistent techniques for portfolio optimization. Keywords: Iterative solvers, portfolio optimization, successive over relaxation, Chebyshev accelaration, Jacobi and Gauss-Seidel algorithms