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arXiv · 2311.17038

Extension of Minimax for Algorithmic Lower Bounds

Jason Hartline·Aleck Johnsen
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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.

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Jason Hartline, Aleck Johnsen. 2023-11-28. Extension of Minimax for Algorithmic Lower Bounds. https://arxiv.org/abs/2311.17038

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