Robust and Adaptive Sequential Submodular Optimization
Name
1909.11783.pdf
Description
Accepted version
Size
1.1 MB
Format
Adobe PDF
Checksum (MD5)
beadda438ef71f7c3022eca428f493d5
Author(s)
Tzoumas, V
Jadbabaie, A
Pappas, GJ
Date Issued
January 1, 2020
Journal
IEEE Transactions on Automatic Control
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Tzoumas, V, Jadbabaie, A and Pappas, GJ. 2020. "Robust and Adaptive Sequential Submodular Optimization." IEEE Transactions on Automatic Control, 67 (1).
Version
Author's final manuscript
Abstract
IEEE Emerging applications of control, estimation, and machine learning pose resource constraints that limit which of the available sensors, actuators, or data can be simultaneously used. Therefore, researchers have proposed solutions within discrete optimization frameworks where the optimization is performed over finite sets. In this paper, we consider such problems but in adversarial environments, where in every step a number of the chosen elements in the optimization is removed due to failures/attacks. Specifically, we consider for the first time a sequential version of the problem that allows us to observe the failures and adapt, while the attacker also adapts to our response. We call the novel problem Robust Sequential submodular Maximization (RSM). Generally, the problem is computationally hard and no scalable algorithm is known for its solution. In this paper, we propose Robust and Adaptive Maximization (RAM), the first scalable algorithm. RAM adapts in every step to the history of failures. Also, it guarantees a near-optimal performance. Finally, we demonstrate RAM's near-optimality in simulations across various application scenarios, along with its robustness against several failure types, from worst-case to random.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TAC.2020.3046222
