Difference between revisions of "ALENEX"
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{{Event series | {{Event series | ||
|Acronym=ALENEX | |Acronym=ALENEX | ||
− | |Title=Symposium on Algorithm Engineering and Experiments | + | |Title =Symposium on Algorithm Engineering and Experiments |
|has CORE2017 Rank=A | |has CORE2017 Rank=A | ||
− | |Field=Algorithms | + | |Field =Algorithms |
− | | | + | |pageCreator=Soeren |
+ | |pageEditor=Heike.Rohde | ||
+ | |contributionType=1 | ||
}} | }} | ||
''The annual Meeting on Algorithm Engineering and Experiments (ALENEX) provides a forum for the presentation of original research in the design, implementation, and experimental evaluation of algorithms and data structures. Typical contributions include an extensive experimental analysis of nontrivial algorithmic results, ideally bridging the gap between theory and practice.'' | ''The annual Meeting on Algorithm Engineering and Experiments (ALENEX) provides a forum for the presentation of original research in the design, implementation, and experimental evaluation of algorithms and data structures. Typical contributions include an extensive experimental analysis of nontrivial algorithmic results, ideally bridging the gap between theory and practice.'' |
Latest revision as of 21:08, 1 April 2022
ALENEX 



ALENEX | |
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Symposium on Algorithm Engineering and Experiments
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Categories: Algorithms
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CORE Rank (2017): | A |
Avg. acceptance rate: | 25.6 |
Avg. acceptance rate (last 5 years): | 25.6 |
Table of Contents | |
Symposium on Algorithm Engineering and Experiments (ALENEX) has an average acceptance rate of 25.6% (last 5 years 25.6%).
Events
There are 4 events of the series ALENEX known to this wiki: ALENEX 2018, ALENEX 2019, ALENEX 2020, ALENEX 2021
Submission/Acceptance
Locations
The annual Meeting on Algorithm Engineering and Experiments (ALENEX) provides a forum for the presentation of original research in the design, implementation, and experimental evaluation of algorithms and data structures. Typical contributions include an extensive experimental analysis of nontrivial algorithmic results, ideally bridging the gap between theory and practice.