Paper Info
Title
The Impact of Information in Distributed Submodular Maximization
Abstract
The maximization of submodular functions is an NP-Hard problem for certain subclasses of functions, for which a simple greedy algorithm has been shown to guarantee a solution whose quality is within 1/2 of the optimal. When this algorithm is implemented in a distributed way, agents sequentially make decisions based on the decisions of all previous agents. This work explores how limited access to the decisions of previous agents affects the quality of the solution of the greedy algorithm. Specifically, we provide tight upper and lower bounds on how well the algorithm performs, as a function of the information available to each agent. Intuitively, the results show that performance roughly degrades proportionally to the size of the largest group of agents that make decisions independently. Additionally, we consider the case where a system designer is given a set of agents and a global limit on the amount of information that can be accessed. Our results show that the best designs partition the agents into equally sized sets and allow agents to access the decisions of all previous agents within the same set.
Year
DOI
Venue
2019
10.1109/TCNS.2018.2889005
IEEE Transactions on Control of Network Systems
Keywords
DocType
Volume
Greedy algorithms,Robot sensing systems,Linear programming,Optimization,Approximation algorithms,Resource management,Distributed algorithms
Journal
6
Issue
ISSN
Citations 
4
IEEE Transactions on Control of Network Systems, December 2018
3
PageRank 
References 
Authors
0.40
0
4
Name
Order
Citations
PageRank
David Grimsman171.89
Mohd. Shabbir Ali2232.86
João P. Hespanha37674587.34
Jason R. Marden467656.57