Maximization of Approximately Submodular Functions
Abstract
We study the problem of maximizing a function that is approximately submodular under a cardinality constraint. Approximate submodularity implicitly appears in a wide range of applications as in many cases errors in evaluation of a submodular function break submodularity. Say that $F$ is $\eps$-approximately submodular if there exists a submodular function $f$ such that $(1-\eps)f(S) \leq F(S)\leq (1+\eps)f(S)$ for all subsets $S$. We are interested in characterizing the query-complexity of maximizing $F$ subject to a cardinality constraint $k$ as a function of the error level $\eps > 0$. We provide both lower and upper bounds: for $\eps > n^{-1/2}$ we show an exponential query-complexity lower bound. In contrast, when $\eps < {1}/{k}$ or under a stronger bounded curvature assumption, we give constant approximation algorithms.
Cite
Text
Horel and Singer. "Maximization of Approximately Submodular Functions." Neural Information Processing Systems, 2016.Markdown
[Horel and Singer. "Maximization of Approximately Submodular Functions." Neural Information Processing Systems, 2016.](https://mlanthology.org/neurips/2016/horel2016neurips-maximization/)BibTeX
@inproceedings{horel2016neurips-maximization,
title = {{Maximization of Approximately Submodular Functions}},
author = {Horel, Thibaut and Singer, Yaron},
booktitle = {Neural Information Processing Systems},
year = {2016},
pages = {3045-3053},
url = {https://mlanthology.org/neurips/2016/horel2016neurips-maximization/}
}