Maximizing Non-Monotone DR-Submodular Functions with Cardinality Constraints
Abstract
We consider the problem of maximizing a non-monotone DR-submodular function subject to a cardinality constraint. Diminishing returns (DR) submodularity is a generalization of the diminishing returns property for functions defined over the integer lattice. This generalization can be used to solve many machine learning or combinatorial optimization problems such as optimal budget allocation, revenue maximization, etc. In this work we propose the first polynomial-time approximation algorithms for non-monotone constrained maximization. We implement our algorithms for a revenue maximization problem with a real-world dataset to check their efficiency and performance.
Cite
@article{arxiv.1611.09474,
title = {Maximizing Non-Monotone DR-Submodular Functions with Cardinality Constraints},
author = {Ali Khodabakhsh and Evdokia Nikolova},
journal= {arXiv preprint arXiv:1611.09474},
year = {2017}
}
Comments
Error description: The proposed algorithms have running time issues, in particular they are pseudo-polynomial and not fully polynomial-time