# Online Contention Resolution Schemes

arXiv: https://arxiv.org/abs/1508.00142 · HTML: https://mlsanity.com/paper/1508.00142/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2015.

## Built on by (most cited first, 6 of 6)

- [Bandit Algorithms for Prophet Inequality and Pandora's Box](https://mlsanity.com/paper/2211.08586/) (2022)
- [Learning-Augmented Online Bipartite Fractional Matching](https://mlsanity.com/paper/2505.19252/) (2025)
- [Online Primal-Dual Algorithms with Predictions for Packing Problems](https://mlsanity.com/paper/2110.00391/) (2021)
- [Online Combinatorial Allocations and Auctions with Few Samples](https://mlsanity.com/paper/2409.11091/) (2024)
- [Fully Dynamic Online Selection through Online Contention Resolution Schemes](https://mlsanity.com/paper/2301.03099/) (2023)
- [The Competition Complexity of Prophet Inequalities with Correlations](https://mlsanity.com/paper/2409.06868/) (2024)

## Abstract (arXiv)

We introduce a new rounding technique designed for online optimization problems, which is related to contention resolution schemes, a technique initially introduced in the context of submodular function maximization. Our rounding technique, which we call online contention resolution schemes (OCRSs), is applicable to many online selection problems, including Bayesian online selection, oblivious posted pricing mechanisms, and stochastic probing models. It allows for handling a wide set of constraints, and shares many strong properties of offline contention resolution schemes. In particular, OCRSs for different constraint families can be combined to obtain an OCRS for their intersection. Moreover, we can approximately maximize submodular functions in the online settings we consider. We, thus, get a broadly applicable framework for several online selection problems, which improves on previous approaches in terms of the types of constraints that can be handled, the objective functions that can be dealt with, and the assumptions on the strength of the adversary. Furthermore, we resolve two open problems from the literature; namely, we present the first constant-factor constrained oblivious posted price mechanism for matroid constraints, and the first constant-factor algorithm for weighted stochastic probing with deadlines.