paper-with-me

Papers

The Complexity of Optimizing Atomic Congestion

2023-12-15 · Cornelius Brand, Robert Ganian, Subrahmanyam Kalyanasundaram, Fionn Mc Inerney

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.

📄 PDF Abstract BibTeX arXiv:2312.10219

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

SET Dynamic Sparse Training method where weight mask is updated randomly periodically

Similar Papers 제목 키워드 기반

Congestion Reduction in EV Charger Placement Using Traffic Equilibrium Models

2025-12-12 · Semih Kara, Yasin Sonmez, Can Kizilkale, Alex Kurzhanskiy 외 arxiv

Growing EV adoption can worsen traffic conditions if chargers are sited without regard to their impact on congestion. We study how to strategically place EV chargers to reduce congestion using two equilibrium models: one…

A Comparative Study of Loss Functions: Traffic Predictions in Regular and Congestion Scenarios

2023-08-29 · Yangxinyu Xie, Tanwi Mallick

Spatiotemporal graph neural networks have achieved state-of-the-art performance in traffic forecasting. However, they often struggle to forecast congestion accurately due to the limitations of traditional loss functions.…

imbalanced classificationManagement

Generalized Mirror Descents in Congestion Games

2016-05-25 · Po-An Chen, Chi-Jen Lu

Different types of dynamics have been studied in repeated game play, and one of them which has received much attention recently consists of those based on "no-regret" algorithms from the area of machine learning. It is k…

Learning Optimal Tax Design in Nonatomic Congestion Games

2024-02-12 · Qiwen Cui, Maryam Fazel, Simon S. Du

In multiplayer games, self-interested behavior among the players can harm the social welfare. Tax mechanisms are a common method to alleviate this issue and induce socially optimal behavior. In this work, we take the ini…

A Deep Reinforcement Learning Framework for Optimizing Congestion Control in Data Centers

2023-01-29 · Shiva Ketabi, Hongkai Chen, Haiwei Dong, Yashar Ganjali

Various congestion control protocols have been designed to achieve high performance in different network environments. Modern online learning solutions that delegate the congestion control actions to a machine cannot pro…

Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)