New Approximation Algorithms for Minimum Enclosing Convex Shapes

Given $n$ points in a $d$ dimensional Euclidean space, the Minimum Enclosing Ball (MEB) problem is to find the ball with the smallest radius which contains all $n$ points. We give a $O(ndQcal/sqrt{epsilon})$ approximation algorithm for producing an enclosing ball whose radius is at most $epsilon$ away from the optimum (where $Qcal$ is an upper bound on the norm of the points). This improves existing results using emph{coresets}, which yield a $O(nd/epsilon)$ greedy algorithm. Finding the Minimum Enclosing Convex Polytope (MECP) is a related problem wherein a convex polytope of a fixed shape is given and the aim is to find the smallest magnification of the polytope which encloses the given points. For this problem we present a $O(mndQcal/epsilon)$ approximation algorithm, where $m$ is the number of faces of the polytope. Our algorithms borrow heavily from convex duality and recently developed techniques in non-smooth optimization, and are in contrast with existing methods which rely on geometric arguments. In particular, we specialize the excessive gap framework of citet{Nesterov05a} to obtain our results.