Abstract:

Coordinate descent method is a unconstrained optimization technique. When it is applied to support vector machine (SVM), at each step the method updates one component of w by solving a one-variable sub-problem while fixing other components. All components of w update after one iteration. Then go to next iteration. Though the method converges and converges fast in the beginning, it converges slow for final convergence. To improve the
speed of final convergence of coordinate descent method, Hooke and Jeeves algorithm which adds pattern search after every iteration in coordinate descent method was applied to SVM and a global Newton algorithm was used to solve one-variable sub-problems. We proved the convergence of the algorithm. Experimental results show Hooke and Jeeves’ method does accelerate convergence specially for final convergence and achieves higher testing accuracy more quickly in classification.