统一算法应对交易期限不确定，实现最优收益。
Knowing When to Stop Matters: A Unified Algorithm for Online Conversion under Horizon Uncertainty
- 提出统一算法，适配已知、部分已知、未知三种期限场景。
- 在每步交易量受限条件下，仍保持最优竞争比。
- 可融合预测信息，在预测准确时近优，否则保安全底线。
本文研究在线转换问题，即在动态价格下逐次交易可分资源（如能源）以最大化利润。核心挑战在于期限不确定性：交易周期可能已知、中途揭示或完全未知。本文提出一种统一算法，在三种期限模型下均达到最优竞争保证，并考虑实际约束如每步最大交易量限制。此外，算法扩展为学习增强版本，利用期限预测自适应权衡性能：预测准确时接近最优，预测错误时仍保持强保障。该工作深化了对不同期限不确定性下在线转换的理解，提供了更贴近现实的决策策略。
原文摘要 · Abstract (English)
This paper investigates the online conversion problem, which involves sequentially trading a divisible resource (e.g., energy) under dynamically changing prices to maximize profit. A key challenge in online conversion is managing decisions under horizon uncertainty, where the duration of trading is either known, revealed partway, or entirely unknown. We propose a unified algorithm that achieves optimal competitive guarantees across these horizon models, accounting for practical constraints such as box constraints, which limit the maximum allowable trade per step. Additionally, we extend the algorithm to a learning-augmented version, leveraging horizon predictions to adaptively balance performance: achieving near-optimal results when predictions are accurate while maintaining strong guarantees when predictions are unreliable. These results advance the understanding of online conversion under various degrees of horizon uncertainty and provide more practical strategies to address real world constraints.
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