Thin Trees via $k$-Respecting Cut Identities
October 14, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mohit Daga
arXiv ID
2510.12050
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.DM,
math.CO
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Thin spanning trees lie at the intersection of graph theory, approximation algorithms, and combinatorial optimization. They are central to the long-standing \emph{thin tree conjecture}, which asks whether every $k$-edge-connected graph contains an $O(1/k)$-thin tree, and they underpin algorithmic breakthroughs such as the $O(\log n/\log\log n)$-approximation for ATSP. Yet even the basic algorithmic task of \emph{verifying} that a given tree is thin has remained elusive: checking thinness requires reasoning about exponentially many cuts, and no efficient certificates have been known. We introduce a new machinery of \emph{$k$-respecting cut identities}, which express the weight of every cut that crosses a spanning tree in at most $k$ edges as a simple function of pairwise ($2$-respecting) cuts. This yields a tree-local oracle that, after $O(n^2)$ preprocessing, evaluates such cuts in $O_k(1)$ time. Building on this oracle, we give the first procedure to compute the exact $k$-thinness certificate $Ξ_k(T)$ of any spanning tree for fixed $k$ in time $\tilde O(n^2+n^k)$, outputting both the certificate value and a witnessing cut. Beyond general graphs, our framework yields sharper guarantees in structured settings. In planar graphs, duality with cycles and dual girth imply that every spanning tree admits a verifiable certificate $Ξ_k(T)\le k/Ξ»$ (hence $O(1/Ξ»)$ for constant $k$). In graphs embedded on a surface of genus $Ξ³$, refined counting gives certified (per-cut) bounds $O((\log n+Ξ³)/Ξ»)$ via the same ensemble coverage.
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