Free access
Proceedings
2020 Proceedings of the Twenty-Second Workshop on Algorithm Engineering and Experiments (ALENEX)

Engineering Top-Down Weight-Balanced Trees

Abstract

Weight-balanced trees are a popular form of self-balancing binary search trees. Their popularity is due to desirable guarantees, for example regarding the required work to balance annotated trees.
While usual weight-balanced trees perform their balancing operations in a bottom-up fashion after a modification to the tree is completed, there exists a top-down variant which performs these balancing operations during descend. This variant has so far received only little attention. We provide an in-depth analysis and engineering of these top-down weight-balanced trees, demonstrating their superior performance. We also gaining insights into how the balancing parameters necessary for a weight-balanced tree should be chosen — with the surprising observation that it is often beneficial to choose parameters which are not feasible in the sense of the correctness proofs for the rebalancing algorithm.

Formats available

You can view the full content in the following formats:

Information & Authors

Information

Published In

cover image Proceedings
2020 Proceedings of the Twenty-Second Workshop on Algorithm Engineering and Experiments (ALENEX)
Pages: 161 - 174
Editors: Guy Blelloch and Irene Finocchi
ISBN (Online): 978-1-611976-00-7

History

Published online: 17 December 2019

Authors

Affiliations

Notes

*
This work was supported by the German Research Foundation (DFG) as part of the Research Training Group GRK 2153: Energy Status Data – Informatics Methods for its Collection, Analysis and Exploitation.

Metrics & Citations

Metrics

Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited By

There are no citations for this item

View Options

View options

PDF

View PDF

Get Access

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share on social media

The SIAM Publications Library now uses SIAM Single Sign-On for individuals. If you do not have existing SIAM credentials, create your SIAM account https://my.siam.org.