An FPTAS for Counting Proper Four-Colorings on Cubic Graphs
November 13, 2016 Β· Declared Dead Β· π ACM-SIAM Symposium on Discrete Algorithms
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Pinyan Lu, Kuan Yang, Chihao Zhang, Minshen Zhu
arXiv ID
1611.04100
Category
cs.DS: Data Structures & Algorithms
Cross-listed
math.CO
Citations
14
Venue
ACM-SIAM Symposium on Discrete Algorithms
Last Checked
3 months ago
Abstract
Graph coloring is arguably the most exhaustively studied problem in the area of approximate counting. It is conjectured that there is a fully polynomial-time (randomized) approximation scheme (FPTAS/FPRAS) for counting the number of proper colorings as long as $q \geq Ξ+ 1$, where $q$ is the number of colors and $Ξ$ is the maximum degree of the graph. The bound of $q = Ξ+ 1$ is the uniqueness threshold for Gibbs measure on $Ξ$-regular infinite trees. However, the conjecture remained open even for any fixed $Ξ\geq 3$ (The cases of $Ξ=1, 2$ are trivial). In this paper, we design an FPTAS for counting the number of proper $4$-colorings on graphs with maximum degree $3$ and thus confirm the conjecture in the case of $Ξ=3$. This is the first time to achieve this optimal bound of $q = Ξ+ 1$. Previously, the best FPRAS requires $q > \frac{11}{6} Ξ$ and the best deterministic FPTAS requires $q > 2.581Ξ+ 1$ for general graphs. In the case of $Ξ=3$, the best previous result is an FPRAS for counting proper 5-colorings. We note that there is a barrier to go beyond $q = Ξ+ 2$ for single-site Glauber dynamics based FPRAS and we overcome this by correlation decay approach. Moreover, we develop a number of new techniques for the correlation decay approach which can find applications in other approximate counting problems.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted