Revisiting the Approximation Bound for Stochastic Submodular Cover
Abstract
Deshpande et al. presented a k(ln R + 1) approximation bound for Stochastic Submodular Cover, where k is the state set size, R is the maximum utility of a single item, and the utility function is integer-valued. This bound is similar to the ln Q/(eta+1) bound given by Golovin and Krause, whose analysis was recently found to have an error. Here Q >= R is the goal utility and eta is the minimum gap between Q and any attainable utility Q' < Q. We revisit the proof of the k(ln R + 1) bound of Deshpande et al., fill in the details of the proof of a key lemma, and prove two bounds for real-valued utility functions: k(ln R_1 + 1) and (ln R_E + 1). Here R_1 equals the maximum ratio between the largest increase in utility attainable from a single item, and the smallest non-zero increase attainable from that same item (in the same state). The quantity R_E equals the maximum ratio between the largest expected increase in utility from a single item, and the smallest non-zero expected increase in utility from that same item. Our bounds apply only to the stochastic setting with independent states.
Cite
Text
Hellerstein and Kletenik. "Revisiting the Approximation Bound for Stochastic Submodular Cover." Journal of Artificial Intelligence Research, 2018. doi:10.1613/JAIR.1.11242Markdown
[Hellerstein and Kletenik. "Revisiting the Approximation Bound for Stochastic Submodular Cover." Journal of Artificial Intelligence Research, 2018.](https://mlanthology.org/jair/2018/hellerstein2018jair-revisiting/) doi:10.1613/JAIR.1.11242BibTeX
@article{hellerstein2018jair-revisiting,
title = {{Revisiting the Approximation Bound for Stochastic Submodular Cover}},
author = {Hellerstein, Lisa and Kletenik, Devorah},
journal = {Journal of Artificial Intelligence Research},
year = {2018},
pages = {265-279},
doi = {10.1613/JAIR.1.11242},
volume = {63},
url = {https://mlanthology.org/jair/2018/hellerstein2018jair-revisiting/}
}