Infrequent Resolving Algorithm for Online Linear Programming

Abstract

Online linear programming (OLP) has gained significant attention from both researchers and practitioners because of its extensive applications such as online auctions, network revenue management, order fulfillment, and advertising. Existing OLP algorithms fall into two categories: LP-based algorithms and LP-free algorithms. The former typically guarantees better performance but requires solving a large number of LPs, which could be computationally expensive. In contrast, LP-free algorithms only require first-order computations but induce a worse performance. In this work, we bridge the gap between these two extremes by proposing a well-performing algorithm that solves LPs at a few selected time points and conducts first-order computations at other time points. Specifically, for the case where the inputs are drawn from an unknown finite-support distribution, the proposed algorithm achieves a constant regret (even for the hard “degenerate” case) while solving LPs only [Formula: see text] times over the time horizon T. Moreover, when we are allowed to solve LPs only M times, we design the corresponding schedule such that the proposed algorithm can guarantee a nearly [Formula: see text] regret. Our work highlights the value of resolving both at the beginning and the end of the selling horizon, and provides a novel framework to prove the performance guarantee of the proposed policy under different infrequent resolving schedules. Numerical experiments are conducted to demonstrate the efficiency of the proposed algorithms. Funding: G. Li’s research is partially supported by the Social Sciences and Humanities Research Council of Canada and the Natural Sciences and Engineering Research Council of Canada [Grant DG RGPIN-2021-02973]. Z. Wang’s research is partially supported by the National Natural Science Foundation of China [Grants 72394361 and 72425013], the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001], and the 1 + 1 + 1 CUHK-CUHK(SZ)-GDSTC Joint Collaboration Fund [Grant 2025A0505000079]. J. Zhang is partially supported by the National Natural Science Foundation of China [Grant 72394361] and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2025.0898 .

The authors' abstract, as published at the source. Mathematics of Operations Research, 2026 · DOI ↗