Lower Bounds for Distributed Maximum-Finding Algorithms

Jan K. Pachl; E. Korach; D. Rotem

doi:10.1145/1634.1889

Journal of the ACM

Paper

20 Sep 1984

Lower Bounds for Distributed Maximum-Finding Algorithms

Download paper

Abstract

Tills paper establishes several lower bounds of the form Ω(nlogn) for the number of messages needed to find the maximum label in a circular configuration of n labeled processes with no central controller. © 1984, ACM. All rights reserved.

Demo paper

SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning

Baihan Lin, Guillermo Cecchi, et al.

IJCAI 2023

Conference paper

One-Class Conditional Random Fields for sequential anomaly detection

Yale Song, Zhen Wen, et al.

IJCAI 2013

Conference paper

Reasoning about Noisy Sensors in the Situation Calculus

Fahiem Bacchus, Joseph Y. Halpern, et al.

IJCAI 1995

Poster

Physics-Constrained Deep Learning for Downscaling

Paula Harder, Venkatesh Ramesh, et al.

EGU 2023

View all publications

Abstract

Related

SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning

One-Class Conditional Random Fields for sequential anomaly detection

Reasoning about Noisy Sensors in the Situation Calculus

Physics-Constrained Deep Learning for Downscaling