Analysis of Convergence Property of an SMO Algorithm for Support Vector Regression

摘要

Sequential minimal optimization (SMO) algorithms for support vector regression have recently been proposed. However, the convergence of these algorithms has not been proved so far. In this report, we consider a general SMO algorithm and give a" rigorous proof that it always satisfies the optimality condition within a finite number of iterations.