Lower Bound on the Greedy Approximation Ratio for Adaptive Submodular Cover
Data Structures and Algorithms
2024-05-27 v1 Artificial Intelligence
Machine Learning
Abstract
We show that the greedy algorithm for adaptive-submodular cover has approximation ratio at least 1.3*(1+ln Q). Moreover, the instance demonstrating this gap has Q=1. So, it invalidates a prior result in the paper ``Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization'' by Golovin-Krause, that claimed a (1+ln Q)^2 approximation ratio for the same algorithm.
Cite
@article{arxiv.2405.14995,
title = {Lower Bound on the Greedy Approximation Ratio for Adaptive Submodular Cover},
author = {Blake Harris and Viswanath Nagarajan},
journal= {arXiv preprint arXiv:2405.14995},
year = {2024}
}
Comments
7 pages, 1 figure. arXiv admin note: substantial text overlap with arXiv:2208.08351