The Complexity of Optimizing Atomic Congestion

Authors

  • Cornelius Brand Algorithms & Complexity Theory Group, Regensburg University, Germany
  • Robert Ganian Algorithms and Complexity Group, TU Wien, Austria
  • Subrahmanyam Kalyanasundaram Department of Computer Science and Engineering, IIT Hyderabad, India
  • Fionn Mc Inerney Algorithms and Complexity Group, TU Wien, Austria

DOI:

https://doi.org/10.1609/aaai.v38i18.29982

Keywords:

PRS: Routing, KRR: Computational Complexity of Reasoning, MAS: Multiagent Planning, MAS: Other Foundations of Multi Agent Systems

Abstract

Atomic congestion games are a classic topic in network design, routing, and algorithmic game theory, and are capable of modeling congestion and flow optimization tasks in various application areas. While both the price of anarchy for such games as well as the computational complexity of computing their Nash equilibria are by now well-understood, the computational complexity of computing a system-optimal set of strategies - that is, a centrally planned routing that minimizes the average cost of agents - is severely understudied in the literature. We close this gap by identifying the exact boundaries of tractability for the problem through the lens of the parameterized complexity paradigm. After showing that the problem remains highly intractable even on extremely simple networks, we obtain a set of results which demonstrate that the structural parameters which control the computational (in)tractability of the problem are not vertex-separator based in nature (such as, e.g., treewidth), but rather based on edge separators. We conclude by extending our analysis towards the (even more challenging) min-max variant of the problem.

Published

2024-03-24

How to Cite

Brand, C., Ganian, R., Kalyanasundaram, S., & Mc Inerney, F. (2024). The Complexity of Optimizing Atomic Congestion. Proceedings of the AAAI Conference on Artificial Intelligence, 38(18), 20044-20052. https://doi.org/10.1609/aaai.v38i18.29982

Issue

Section

AAAI Technical Track on Planning, Routing, and Scheduling