Online Vector Balancing and Geometric Discrepancy

12/06/2019 ∙ by Nikhil Bansal, et al. ∙ 0

We consider an online vector balancing question where T vectors, chosen from an arbitrary distribution over [-1,1]^n, arrive one-by-one and must be immediately given a ± sign. The goal is to keep the discrepancy small as possible. A concrete example is the online interval discrepancy problem where T points are sampled uniformly in [0,1], and the goal is to immediately color them ± such that every sub-interval remains nearly balanced. As random coloring incurs Ω(T^1/2) discrepancy, while the offline bounds are Θ((nlog T)^1/2) for vector balancing and 1 for interval balancing, a natural question is whether one can (nearly) match the offline bounds in the online setting for these problems. One must utilize the stochasticity as in the worst-case scenario it is known that discrepancy is Ω(T^1/2) for any online algorithm. Bansal and Spencer recently show an O(√(n)log T) bound when each coordinate is independent. When there are dependencies among the coordinates, the problem becomes much more challenging, as evidenced by a recent work of Jiang, Kulkarni, and Singla that gives a non-trivial O(T^1/loglog T) bound for online interval discrepancy. Although this beats random coloring, it is still far from the offline bound. In this work, we introduce a new framework for online vector balancing when the input distribution has dependencies across coordinates. This lets us obtain a poly(n, log T) bound for online vector balancing under arbitrary input distributions, and a poly(log T) bound for online interval discrepancy. Our framework is powerful enough to capture other well-studied geometric discrepancy problems; e.g., a poly(log^d (T)) bound for the online d-dimensional Tusńady's problem. A key new technical ingredient is an anti-concentration inequality for sums of pairwise uncorrelated random variables.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 13

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.