Brief Announcement: Efficient Collaborative Tree Exploration with Breadth-First Depth-Next
Abstract
References
Index Terms
- Brief Announcement: Efficient Collaborative Tree Exploration with Breadth-First Depth-Next
Recommendations
Brief Announcement: An Exponential Separation Between Randomized and Deterministic Complexity in the LOCAL Model
PODC '16: Proceedings of the 2016 ACM Symposium on Principles of Distributed ComputingOver the past 30 years numerous algorithms have been designed for symmetry breaking problems in the LOCAL model, such as maximal matching, MIS, vertex coloring, and edge-coloring. For most problems the best randomized algorithm is at least exponentially ...
Brief Announcement: Using Read-k Inequalities to Analyze a Distributed MIS Algorithm
PODC '16: Proceedings of the 2016 ACM Symposium on Principles of Distributed ComputingUntil recently, the fastest distributed MIS algorithm, even for simple graphs, e.g., unoriented trees, has been the simple randomized algorithm discovered in the 80s. This algorithm (commonly called Luby's algorithm) computes an MIS in O(log n) rounds (...
Brief Announcement: Symmetry Breaking in the CONGEST Model: Time- and Message-Efficient Algorithms for Ruling Sets
PODC '17: Proceedings of the ACM Symposium on Principles of Distributed ComputingWe study local symmetry breaking problems in the Congest model, focusing on ruling set problems, which generalize the fundamental Maximal Independent Set (MIS) problem. Our work is motivated by the following central question: can we break the long-...
Comments
Information & Contributors
Information
Published In
![cover image ACM Conferences](/cms/asset/3a17ec98-f34f-4520-b9f1-67deb1468cf7/3583668.cover.jpg)
- Chair:
- Rotem Oshman,
- Proceedings Editor:
- Alexandre Nolin,
- Program Chair:
- Magnus Mar Halldorsson,
- Workshop Chair:
- Alkida Balliu
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Short-paper
Funding Sources
- Agence Nationale de la Recherche
- Paris Artificial Intelligence Research Institute
Conference
Acceptance Rates
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 55Total Downloads
- Downloads (Last 12 months)18
- Downloads (Last 6 weeks)0
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in