提出UDuo框架，动态优化在线资源分配的效率与稳定性。
UDuo: Universal Dual Optimization Framework for Online Matching
- 用时间向量建模用户到达变化，捕捉分布偏移。
- 自适应分配策略在多约束场景下表现更优。
- 适合实时定价、动态资源调度等场景。
在预算约束下的在线资源分配，高度依赖对用户到达动态的准确建模。传统方法采用随机用户到达模型，通过暴露用户的分数匹配形式求解近优解，但在环境动态变化时已不再合理。本文提出通用对偶优化框架UDuo，从三个关键创新重思在线分配：(i) 时间用户到达表示向量，显式捕捉用户到达模式与资源消耗动态的分布偏移；(ii) 具有自适应分配策略的资源调度学习器，可泛化至异构约束场景；(iii) 在线时间序列预测方法，能在动态环境中实现渐近最优解并保证约束可行性。实验表明，相较于传统随机到达模型，UDuo在真实世界定价任务中提升效率、加速收敛，同时保持一般在线分配问题的严格理论有效性。
原文摘要 · Abstract (English)
Online resource allocation under budget constraints critically depends on proper modeling of user arrival dynamics. Classical approaches employ stochastic user arrival models to derive near-optimal solutions through fractional matching formulations of exposed users for downstream allocation tasks. However, this is no longer a reasonable assumption when the environment changes dynamically. In this work, We propose the Universal Dual optimization framework UDuo, a novel paradigm that fundamentally rethinks online allocation through three key innovations: (i) a temporal user arrival representation vector that explicitly captures distribution shifts in user arrival patterns and resource consumption dynamics, (ii) a resource pacing learner with adaptive allocation policies that generalize to heterogeneous constraint scenarios, and (iii) an online time-series forecasting approach for future user arrival distributions that achieves asymptotically optimal solutions with constraint feasibility guarantees in dynamic environments. Experimental results show that UDuo achieves higher efficiency and faster convergence than the traditional stochastic arrival model in real-world pricing while maintaining rigorous theoretical validity for general online allocation problems.
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