Deterministic approximation for the volume of the truncated fractional matching polytope
September 11, 2024 Β· Declared Dead Β· π Information Technology Convergence and Services
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Heng Guo, Vishvajeet N
arXiv ID
2409.07283
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.DM,
math.CO
Citations
1
Venue
Information Technology Convergence and Services
Last Checked
4 months ago
Abstract
We give a deterministic polynomial-time approximation scheme (FPTAS) for the volume of the truncated fractional matching polytope for graphs of maximum degree $Ξ$, where the truncation is by restricting each variable to the interval $[0,\frac{1+Ξ΄}Ξ]$, and $Ξ΄\le \frac{C}Ξ$ for some constant $C>0$. We also generalise our result to the fractional matching polytope for hypergraphs of maximum degree $Ξ$ and maximum hyperedge size $k$, truncated by $[0,\frac{1+Ξ΄}Ξ]$ as well, where $Ξ΄\le CΞ^{-\frac{2k-3}{k-1}}k^{-1}$ for some constant $C>0$. The latter result generalises both the first result for graphs (when $k=2$), and a result by Bencs and Regts (2024) for the truncated independence polytope (when $Ξ=2$). Our approach is based on the cluster expansion technique.
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