Maximization of Approximately Submodular Functions
–Neural Information Processing Systems
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.
Neural Information Processing Systems
Dec-31-2016
- Country:
- Europe > Spain > Catalonia > Barcelona Province > Barcelona (0.04)
- Genre:
- Research Report (0.47)
- Technology:
- Information Technology > Artificial Intelligence
- Machine Learning (1.00)
- Natural Language > Information Retrieval (0.55)
- Representation & Reasoning > Optimization (0.68)
- Vision (0.68)
- Information Technology > Artificial Intelligence