Generalized SMO-style decomposition algorithms
In: COLT 2007, 13-15 June 2007, San Diego, California.
Sequential Minimal Optimization (SMO) (Platt:1999) is a major tool for solving convex quadratic optimization problems induced by Support Vector Machines (SVMs). It is based on the idea to iterativley solve subproblems of size two.
In this work we will give a characterization of convex quadratic optimization problems, which can be solved with the SMO technique as well.
In addition we will present an efficient 1/m-rate-certifying pair selection algorithm (Hush and Scovel:2003, List and Simon:2006) leading to polynomial-time convergence rates for such problems.