Extending Compact-Table to Negative and Short Tables

Authors: HŽlne Verhaeghe, Christophe Lecoutre, Pierre Schaus

AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Our experimental results show the interest of using this fast general algorithm.
Researcher Affiliation Academia 1UCLouvain, ICTEAM, Place Sainte Barbe 2, 1348 Louvain-la-Neuve, Belgium, {firstname.lastname}@uclouvain.be 2CRIL-CNRS UMR 8188, Universit e d Artois, F-62307 Lens, France, lecoutre@cril.fr
Pseudocode Yes Algorithm 1: Class Constraint CT; Algorithm 2: Class Constraint CTneg; Algorithm 3: Function nb1s(bs: Bitset); Algorithm 4: Function nb1s (bs: Bitset)
Open Source Code Yes We have implemented all algorithms described in this paper, namely, CT, CT , CTneg and CT neg in the Oscar solver (Osca R Team 2012), using 64-bit words (Long). ... Osca R: Scala in OR. Available from https://bitbucket.org/oscarlib/oscar.
Open Datasets No Unfortunately, to the best of our knowledge, there are no available benchmarks for positive and negative short tables. ... Consequently, we have generated random tables, varying the tightness of the tables... The paper describes generating random tables but does not provide access information for these generated datasets.
Dataset Splits No The paper describes the parameters used to generate the random tables (e.g., '600 instances, each with 20 variables whose domain sizes range from 5 to 7'), but it does not specify how these instances were split into training, validation, or test sets.
Hardware Specification No The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running experiments.
Software Dependencies No We have implemented all algorithms described in this paper, namely, CT, CT , CTneg and CT neg in the Oscar solver (Osca R Team 2012), using 64-bit words (Long). The paper mentions the Oscar solver but does not provide a specific version number for it or any other software dependency.
Experiment Setup Yes The series we used contains 600 instances, each with 20 variables whose domain sizes range from 5 to 7, and 40 random positive short table constraints of arities 6 or 7, each table having a tightness comprised between 0.5% and 2% and a proportion of short tuples equal to 1%, 5%, 10% and 20%.