Competitive Algorithms for Online Budget-Constrained Continuous DR-Submodular Problems
Optimization and Control
2019-07-02 v1 Machine Learning
Machine Learning
Abstract
In this paper, we study a certain class of online optimization problems, where the goal is to maximize a function that is not necessarily concave and satisfies the Diminishing Returns (DR) property under budget constraints. We analyze a primal-dual algorithm, called the Generalized Sequential algorithm, and we obtain the first bound on the competitive ratio of online monotone DR-submodular function maximization subject to linear packing constraints which matches the known tight bound in the special case of linear objective function.
Cite
@article{arxiv.1907.00312,
title = {Competitive Algorithms for Online Budget-Constrained Continuous DR-Submodular Problems},
author = {Omid Sadeghi and Reza Eghbali and Maryam Fazel},
journal= {arXiv preprint arXiv:1907.00312},
year = {2019}
}
Comments
Submitted to NeurIPS 2019