Subadditive Load Balancing
Data Structures and Algorithms
2019-08-27 v1
Abstract
Set function optimization is essential in AI and machine learning. We focus on a subadditive set function that generalizes submodularity, and examine the subadditivity of non-submodular functions. We also deal with a minimax subadditive load balancing problem, and present a modularization-minimization algorithm that theoretically guarantees a worst-case approximation factor. In addition, we give a lower bound computation technique for the problem. We apply these methods to the multi-robot routing problem for an empirical performance evaluation.
Cite
@article{arxiv.1908.09135,
title = {Subadditive Load Balancing},
author = {Kiyohito Nagano and Akihiro Kishimoto},
journal= {arXiv preprint arXiv:1908.09135},
year = {2019}
}
Comments
17 pages, 3 figures