To solve the large size quadratic programming (QP) problems arising in support vector regression (SVR) efficiently, decomposition methods are usually used. In a decomposition method, a large QP problem is decomposed into a series of smaller QP subproblems, which can be solved much faster than the original one. In this report, we consider the global convergence of decomposition methods for SVR. We will show the decomposition methods for the convex programming problem formulated by Flake and Lawrence always stop within a finite number of iterations.