A comparison of the triangle algorithm and sequential minimal optimization algorithm for solving the hard margin problem

In this article we consider the problem of testing, for two nite sets of points in the Euclidean space, if their convex hulls are disjoint and computing an optimal supporting hyperplane if so. This is a fundamental problem of classi cation in machine learning known as the hard-margin SVM. The problem can be formulated as a quadratic programming problem. The SMO algorithm [1] is the current state of art algorithm for solving it, but it does not answer the question of separability. An alternative to solving both problems is the triangle algorithm [2], a geometrically inspired algorithm, initially described for the convex hull membership problem [3], a fundamental problem in linear programming. First, we describe the experimental performance of the triangle algorithm for testing the intersection of two convex hulls. Next, we compare the performance of triangle algorithm with SMO for nding the optimal supporting hyperplane. Based on experimental results ranging up to 5000 points in each set in dimensions up to 10000, the triangle algorithm outperforms SMO.M.S.Includes bibliographical referencesby Mayank Gupt