Summary
The paper studies a problem where numbers arrive one by one in a random order and must be placed immediately in an ordered list to keep close neighbors as similar as possible. The authors show that any method trying to arrange these numbers online will have a cost that grows roughly like the logarithm of the number of numbers, and they provide an algorithm that achieves this cost with high probability. They also prove this is the best possible performance for any online method. Their approach uses information from the numbers seen so far to predict where to place future ones, balancing flexibility to handle local fluctuations. Overall, the paper provides a mathematical guarantee on how well online sorting can work under random arrival conditions.
What this means in practice
- •For real-time data engineers: Design streaming systems that insert incoming values to minimize adjacent difference cost with predictable logarithmic overhead.
- •For signal processing teams: Implement online ordering of received signals or measurements where arrivals are random and minimizing jumps between consecutive values is crucial.
A theory result. No direct application yet.
Authors
Chenglin Fan, Mingyu Kang
Abstract
In random order online sorting, a fixed multiset of $n$ real numbers arrives in a uniformly random order. Each value must be placed immediately and irrevocably into an empty position of an array with exactly $n$ positions. The objective is to minimize the sum of the absolute differences between consecutive entries. If the entire input were known in advance, sorting the values would be optimal and would have cost equal to the range of the input. We prove that the optimal worst case competitive ratio in this model is $Θ(\log n)$ with high probability. In particular, we give a single online algorithm that achieves an $O(\log n)$ competitive ratio with high probability for every fixed input, and we show that every randomized online algorithm has some fixed input on which an $Ω(\log n)$ ratio is unavoidable with high probability. The same upper bound also holds for independent samples from an arbitrary unknown distribution on the real line. The algorithm uses the random arrival order to learn the relative positions of future values from the values seen so far. It maintains a hierarchy of small regions of the input order and allows an arriving value to use nearby space when its preferred region is full. This flexibility avoids the need to reserve large amounts of space for unlikely local fluctuations. The analysis shows that large displacements become rapidly less likely as their scale grows, leading to the $O(\log n)$ high probability bound. A matching lower bound shows that no randomized online algorithm can improve the logarithmic dependence in the worst case.