Re-Pair In Small Space
August 14, 2019 Β· Declared Dead Β· π Data Compression Conference
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Dominik KΓΆppl, Tomohiro I, Isamu Furuya, Yoshimasa Takabatake, Kensuke Sakai, Keisuke Goto
arXiv ID
1908.04933
Category
cs.DS: Data Structures & Algorithms
Citations
4
Venue
Data Compression Conference
Last Checked
4 months ago
Abstract
Re-Pair is a grammar compression scheme with favorably good compression rates. The computation of Re-Pair comes with the cost of maintaining large frequency tables, which makes it hard to compute Re-Pair on large scale data sets. As a solution for this problem we present, given a text of length $n$ whose characters are drawn from an integer alphabet, an $O(n^2) \cap O(n^2 \lg \log_Οn \lg \lg \lg n / \log_Οn)$ time algorithm computing Re-Pair in $n \lg \max(n,Ο)$ bits of space including the text space, where $Ο$ is the number of terminals and non-terminals. The algorithm works in the restore model, supporting the recovery of the original input in the time for the Re-Pair computation with $O(\lg n)$ additional bits of working space. We give variants of our solution working in parallel or in the external memory model.
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