Randomized Learning-Augmented Auctions with Revenue Guarantees
Abstract
The #2-SAT and #3-SAT problems involve counting the number of satisfying assignments (also called models) for instances of 2-SAT and 3-SAT, respectively. In 2010, Zhou et al. (https://doi.org/10.1609/aaai.v24i1.7537) proposed an O*(1.1892^m)-time algorithm for #2-SAT and an efficient approach for #3-SAT, where m denotes the number of clauses. In this paper, we show that the weighted versions of #2-SAT and #3-SAT can be solved in O*(1.1082^m) and O*(1.4423^m) time, respectively. These results directly apply to the unweighted cases and achieve substantial improvements over the previous results. These advancements are enabled by the introduction of novel reduction rules, a refined analysis of branching operations, and the application of path decompositions on the primal and dual graphs of the formula.
Cite
Text
Caragiannis and Kalantzis. "Randomized Learning-Augmented Auctions with Revenue Guarantees." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/297Markdown
[Caragiannis and Kalantzis. "Randomized Learning-Augmented Auctions with Revenue Guarantees." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/caragiannis2024ijcai-randomized/) doi:10.24963/ijcai.2024/297BibTeX
@inproceedings{caragiannis2024ijcai-randomized,
title = {{Randomized Learning-Augmented Auctions with Revenue Guarantees}},
author = {Caragiannis, Ioannis and Kalantzis, Georgios},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {2687-2694},
doi = {10.24963/ijcai.2024/297},
url = {https://mlanthology.org/ijcai/2024/caragiannis2024ijcai-randomized/}
}