Padovan heaps
February 27, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Vladan Majerech
arXiv ID
1902.10812
Category
cs.DS: Data Structures & Algorithms
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
We analyze priority queues of Fibonacci family. The paper is inspired by Violation heap [1], where A. Elmasry saves one pointer in representation of Fibonacci heap nodes while achieving the same amortized bounds as Fibonacci heaps [2] of M. L. Fredman and R. E. Tarjan. Unfortunately author forces the heaps to be wide, what goes against optimal heap principles. Our goal is to achieve the same result, but with much narrower heaps. We follow the principle of superexpensive comparison so we try to remember results of all comparisons and never compare elements that cannot be minimal. We delay comparisons as long as possible. Actually I have always want to share superexpensive comparison principle ideas, discovery of Padovan heaps allowed me to do so. Of course saving one pointer is not that big goal, but I hope the presented reasoning and amortized analysis of the resulting heaps is worth a publication.
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