Convergence Proof of a Sequential Minimal Optimization Algorithm for Support Vector Regression

Jun Guo, Norikazu Takahashi, Tetsuo Nishi

Abstract: A sequential minimal optimization (SMO) algorithm for support vector regression (SVR) has recently been proposed by Flake and Lawrence. However, the convergence of their algorithm has not been proved so far. In this paper, we consider an SMO algorithm, which deals with the same optimization problem as Flake and Lawrence's SMO, and give a rigorous proof that it always stops within a finite number of iterations.