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{{Event Series | {{Event Series | ||
|Acronym=ALENEX | |Acronym=ALENEX | ||
| − | |Title=Symposium on Algorithm Engineering and Experiments | + | |Title=SIAM Symposium on Algorithm Engineering and Experiments |
| + | |Academic Field=Algorithm | ||
| + | |DOI=10.25798/rmwz-hk18 | ||
|pageCreator=User:Curator 89 | |pageCreator=User:Curator 89 | ||
|pageEditor=User:Curator 27 | |pageEditor=User:Curator 27 | ||
|contributionType=1 | |contributionType=1 | ||
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}} | }} | ||
| − | {{Event Series Related Identifier}} | + | {{Event Series Related Identifier |
| + | |DBLP Series ID=alenex | ||
| + | }} | ||
| + | {{Maintainer | ||
| + | |Contributor Type=organization | ||
| + | |Organization=Society for Industrial and Applied Mathematics | ||
| + | }} | ||
{{Event Series CORE Ranking | {{Event Series CORE Ranking | ||
|CORE Rank Year=2017 | |CORE Rank Year=2017 | ||
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}} | }} | ||
{{S Event Series}} | {{S Event Series}} | ||
| + | {{Main Image}} | ||
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''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 13:45, 21 February 2023
Events
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.
