Polynomial-Time Decomposition Algorithms for Support Vector Machines

Abstract

This paper studies the convergence properties of a general class of decomposition algorithms for support vector machines (SVMs). We provide a model algorithm for decomposition, and prove necessary and sufficient conditions for stepwise improvement of this algorithm. We introduce a simple “rate certifying” condition and prove a polynomial-time bound on the rate of convergence of the model algorithm when it satisfies this condition. Although it is not clear that existing SVM algorithms satisfy this condition, we provide a version of the model algorithm that does. For this algorithm we show that when the slack multiplier C satisfies √1/2 ≤ C ≤ mL , where m is the number of samples and L is a matrix norm, then it takes no more than 4 LC 2 m 4 /∈ iterations to drive the criterion to within ∈ of its optimum.