# Adwords in a Panorama

arXiv: https://arxiv.org/abs/2009.04114 · HTML: https://mlsanity.com/paper/2009.04114/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Primal-Dual Algorithms with Predictions for Online Bounded Allocation and Ad-Auctions Problems](https://mlsanity.com/paper/2402.08701/) (2024)
- [Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms](https://mlsanity.com/paper/2302.05005/) (2023)
- [Online Budgeted Matching with General Bids](https://mlsanity.com/paper/2411.04204/) (2024)
- [Online Multi-Class Selection with Group Fairness Guarantee](https://mlsanity.com/paper/2510.21055/) (2025)

## Abstract (arXiv)

Three decades ago, Karp, Vazirani, and Vazirani (STOC 1990) defined the online matching problem and gave an optimal 1-frac1e ≈ 0.632-competitive algorithm. Fifteen years later, Mehta, Saberi, Vazirani, and Vazirani (FOCS 2005) introduced the first generalization called AdWords driven by online advertising and obtained the optimal 1-frac1e competitive ratio in the special case of small bids. It has been open ever since whether there is an algorithm for general bids better than the 0.5-competitive greedy algorithm. This paper presents a 0.5016-competitive algorithm for AdWords, answering this open question on the positive end. The algorithm builds on several ingredients, including a combination of the online primal dual framework and the configuration linear program of matching problems recently explored by Huang and Zhang (STOC 2020), a novel formulation of AdWords which we call the panorama view, and a generalization of the online correlated selection by Fahrbach, Huang, Tao, and Zadimorghaddam (FOCS 2020) which we call the panoramic online correlated selection.