Rigorous Proof of Termination of SMO Algorithm for Support Vector Machines
Norikazu Takahashi, Tetsuo Nishi · IEEE Transactions on Neural Networks · 2005
Sequential minimal optimization (SMO) algorithm is one of the simplest decomposition methods for learning of support vector machines (SVMs). Keerthi and Gilbert have recently studied the convergence property of SMO algorithm and given a proof that SMO algorithm always stops within a finite number of iterations. In this letter, we point out the incompleteness of their proof and give a more rigorous proof.