Improved 2-Approximate Shortest Paths for close vertex pairs
July 26, 2025 Β· Declared Dead Β· π IEEE Annual Symposium on Foundations of Computer Science
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Manoj Gupta
arXiv ID
2507.19859
Category
cs.DS: Data Structures & Algorithms
Citations
1
Venue
IEEE Annual Symposium on Foundations of Computer Science
Last Checked
4 months ago
Abstract
An influential result by Dor, Halperin, and Zwick (FOCS 1996, SICOMP 2000) implies an algorithm that can compute approximate shortest paths for all vertex pairs in $\tilde{O}(n^{2+O\left(\frac{1}{k}\right )})$ time, ensuring that the output distance is at most twice the actual shortest path, provided the pairs are at least $k$ apart, where $k \ge 2$. We present the first improvement on this result in over 25 years. Our algorithm achieves roughly same $\tilde{O}(n^{2+\frac{1}{k}})$ runtime but applies to vertex pairs merely $O(\log k)$ apart, where $\log k \ge 1$. When $k=\log n$, the running time of our algorithm is $\tilde{O}(n^2)$ and it works for all pairs at least $O(\log \log n)$ apart. Our algorithm is combinatorial, randomized, and returns correct results for all pairs with a high probability.
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