Extension of Minimax for Algorithmic Lower Bounds
Computer Science and Game Theory
2023-11-29 v1 Data Structures and Algorithms
Abstract
This paper considers the use of Yao's Minimax Theorem in robust algorithm design, e.g., for online algorithms, where the algorithm designer aims to minimize the ratio of the algorithm's performance to the optimal performance. When applying Minimax, the objective becomes the expectation of these ratios. The main result of the paper is that it is equally valid for the objective (after applying Minimax) to be the ratio of the expectations.
Cite
@article{arxiv.2311.17038,
title = {Extension of Minimax for Algorithmic Lower Bounds},
author = {Jason Hartline and Aleck Johnsen},
journal= {arXiv preprint arXiv:2311.17038},
year = {2023}
}