Maximization of Approximately Submodular Functions Yaron Singer Harvard University
–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 ε-approximately submodular if there exists a submodular function f such that (1 ε)f(S) F (S) (1+ε)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 ε > 0. We provide both lower and upper bounds: for ε > n
Neural Information Processing Systems
Mar-12-2024, 13:44:02 GMT
- Country:
- Europe > Spain > Catalonia > Barcelona Province > Barcelona (0.04)
- Genre:
- Research Report (0.47)
- Technology:
- Information Technology > Artificial Intelligence
- Machine Learning (1.00)
- Representation & Reasoning > Optimization (0.68)
- Vision (0.68)
- Information Technology > Artificial Intelligence