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Philippe Van Kessel

dblp:117/4961 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Algorithms and data structures · 100%
Artificial intelligence
1 paper

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithms and data structures › signal processing algorithms
filtering algorithms
0.112012
Filtering Algorithms Based on the Word-RAM Model · AAAI 2012

Methods — techniques the papers use, named apart from their topics

bitset encoding · 0.3word RAM model · 0.1Word-RAM model · 0.1
YearPublicationVenuePosition
2012 Filtering Algorithms Based on the Word-RAM Model
abstract
The Word-RAM is a model of computation that takes into account the capacity of a computer to manipulate a word of w bits with a single instruction. Many modern constraint solvers use a bitset data structure to encode the values contained in the variable domains. Using the algorithmic techniques developed for the Word-RAM, we propose new filtering algorithms that can prune Opwq values from a domain in a single instruction. Experiments show that on a processor with w = 64, the new filtering algorithms that enforce domain consis- tency on the constraints A + B = C, |A - B| = C and ALL-DIFFERENT can offer a speed up of a factor 10.
Philippe Van Kessel, Claude-Guy Quimper
AAAI1