UDuo: Universal Dual Optimization Framework for Online Matching

TL;DR

UDuo introduces a universal dual optimization framework for online matching that adapts to dynamic user arrival patterns, improving efficiency and convergence in real-world resource allocation scenarios.

Contribution

The paper presents a novel framework that models distribution shifts, employs adaptive resource pacing, and forecasts user arrivals, addressing limitations of traditional stochastic models in dynamic environments.

Findings

UDuo outperforms traditional models in efficiency and convergence.

It maintains theoretical guarantees in dynamic settings.

Experimental results validate its practical effectiveness.

Abstract

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.