A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
We present an algorithm selection framework based on machine learning for the exact computation of <i>treewidth</i>, an intensively studied graph parameter that is NP-hard to compute. Specifically, we analyse the comparative performance of three state-of-the-art exact treewidth algorithm...
Main Authors: | Borislav Slavchev, Evelina Masliankova, Steven Kelk |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-09-01
|
Series: | Algorithms |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-4893/12/10/200 |
Similar Items
-
Treewidth : algorithmic, combinatorial, and practical aspects
by: Baste, Julien
Published: (2017) -
The Treewidth of Induced Graphs of Conditional Preference Networks Is Small
by: Jie Liu, et al.
Published: (2016-02-01) -
Width, Depth, and Space: Tradeoffs between Branching and Dynamic Programming
by: Li-Hsuan Chen, et al.
Published: (2018-07-01) -
圖之和弦圖數與樹寬
by: 游朝凱 -
Practical Access to Dynamic Programming on Tree Decompositions
by: Max Bannach, et al.
Published: (2019-08-01)