Dynamic streaming algorithms for ε-kernels

Introduced by Agarwal, Har-Peled, and Varadarajan [J. ACM, 2004], an ε-kernel of a point set is a coreset that can be used to approximate the width, minimum enclosing cylinder, minimum bounding box, and solve various related geometric optimization problems. Such coresets form one of the most important tools in the design of linear-time approximation algorithms in computational geometry, as well as efficient insertion-only streaming algorithms and dynamic (non-streaming) data structures. In this paper, we continue the theme and explore dynamic streaming algorithms (in the so-called turnstile model). Andoni and Nguyen [SODA 2012] described a dynamic streaming algorithm for maintaining a (1 + ε)-approximation of the width using O(polylog U) space and update time for a point set in [U]^d for any constant dimension d and any constant ε > 0. Their sketch, based on a polynomial method, does not explicitly maintain an ε-kernel. We extend their method to maintain an ε-kernel, and at the same time reduce some of logarithmic factors. As an application, we obtain the first randomized dynamic streaming algorithm for the width problem (and related geometric optimization problems) that supports k outliers, using poly(k, log U) space and time.