Greedy Vector Balancing

2026-06-16

AI Summary

This work addresses the online vector balancing problem, where vectors arrive sequentially and must be assigned ±1 signs to minimize the maximum Euclidean norm of any prefix sum. The authors propose and analyze a Euclidean greedy algorithm that, at each step, selects a sign ensuring the inner product between the current vector and the current prefix sum is non-positive, thereby controlling cumulative discrepancy. For the first time, they establish a constant upper bound—depending only on the dimension and not on the number of vectors—for this greedy strategy over finite vector sets. The proof relies on constructing bounded T-absorbing convex bodies based on chains of subspaces. In the case of unit vectors, they prove an upper bound of $(2/\delta_T)^{d-1}$ on the prefix sum norm and complement it with a lower bound of $\Omega(\sqrt{d}/\delta_T)$. These results yield polynomial-time solvability for certain scheduling problems.

Abstract

In online vector balancing, vectors $t_1,\dots,t_n$ arrive one by one from a given set $T$ and the goal is to assign signs $s_1,\dots,s_n\in\{\pm1\}$ in an online manner so as to minimize the largest norm of any signed prefix sum $\sum_{i=1}^ks_i t_i$, $k \in [n]$. In this paper, we analyze the natural Euclidean greedy vector balancing algorithm for this problem: at each step $k$, the sign $s_k\in\{\pm1\}$ is chosen so that $s_k t_k$ has non-positive inner product with $\sum_{i=1}^{k-1} s_i\cdot t_i$. Our main result is the first finite bound, independent of the sequence length $n$, on the performance of greedy whenever $T$ is finite. When $T \subset \mathbb{R}^d$ consists of unit vectors, we prove that the signed sums produced by greedy have Euclidean norm at most $(2/δ_T)^{d-1}$, where $δ_T$ is the minimum non-zero distance between vectors in $T$ and subspaces spanned by vectors in $T$. The same upper bound holds when the sequences are composed of scaled down vectors in $T$. We also provide a simple set $T$ for which $Ω(\sqrt{d}/δ_T)$ is a lower bound. We analyze the greedy algorithm by proving the existence of a bounded convex $K_T$ that is $T$-absorbing: $\forall x\in K_T$ and $t \in\pm T$, $\langle x,t\rangle\leq0\Rightarrow x+t\in K_T$. We give an explicit construction of a set $K_T$ contained in a ball of radius $(2/δ_T)^{d-1}$, based on chains of subspaces spanned by vectors in $T$, which may be of independent interest. We generalize our greedy vector balancing bound to online vector partitioning, where the sequence $t_1,\dots,t_n$ must be partitioned in an online manner into $p$ subsequences. As an application, we prove a special case of a conjecture of Bosman et al. (arxiv:2402.19259), showing that a lexicographic version of total completion time scheduling under scenarios is polynomial time solvable when the number of scenarios is fixed.

Problem

• online vector balancing
• greedy algorithm
• prefix sum
• finite set
• Euclidean norm

Innovation

• greedy algorithm
• online vector balancing
• T-absorbing convex body
• finite vector set
• online partitioning