Online Discrepancy with Recourse for Vectors and Graphs
share
The vector-balancing problem is a fundamental problem in discrepancy theory: given T vectors in [-1,1]^n, find a signing σ(a) ∈{± 1} of each vector a to minimize the discrepancy ∑_aσ(a) · a _∞. This problem has been extensively studied in the static/offline setting. In this paper we initiate its study in the fully-dynamic setting with recourse: the algorithm sees a stream of T insertions and deletions of vectors, and at each time must maintain a low-discrepancy signing, while also minimizing the amortized recourse (the number of times any vector changes its sign) per update. For general vectors, we show algorithms which almost match Spencer's O(√(n)) offline discrepancy bound, with O(n· polylog T) amortized recourse per update. The crucial idea is to compute a basic feasible solution to the linear relaxation in a distributed and recursive manner, which helps find a low-discrepancy signing. To bound recourse we argue that only a small part of the instance needs to be re-computed at each update. Since vector balancing has also been greatly studied for sparse vectors, we then give algorithms for low-discrepancy edge orientation, where we dynamically maintain signings for 2-sparse vectors. Alternatively, this can be seen as orienting a dynamic set of edges of an n-vertex graph to minimize the absolute difference between in- and out-degrees at any vertex. We present a deterministic algorithm with O(polylog n) discrepancy and O(polylog n) amortized recourse. The core ideas are to dynamically maintain an expander-decomposition with low recourse and then to show that, as the expanders change over time, a natural local-search algorithm converges quickly (i.e., with low recourse) to a low-discrepancy solution. We also give strong lower bounds for local-search discrepancy minimization algorithms.
READ FULL TEXT
