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| ALENEX | |
|---|---|
| Name | ALENEX |
| Discipline | Algorithms and Experimental Analysis |
| Abbreviation | ALENEX |
| Country | International |
| Established | 1999 |
| Frequency | Annual |
ALENEX ALENEX is an annual international symposium on algorithm engineering and experimental algorithmics that emphasizes empirical evaluation of algorithms and data structures. The meeting attracts researchers from institutions such as Massachusetts Institute of Technology, Stanford University, Princeton University, University of California, Berkeley and Carnegie Mellon University and is often associated with larger events like SODA and ICML. Participants include authors from Microsoft Research, Google, IBM Research, Facebook, and Amazon (company) who present experiments complementing theoretical work from venues such as STOC, FOCS, ICALP, and ESA.
ALENEX focuses on experimental algorithmics and algorithm engineering, bringing together practitioners from universities and industry including Cornell University, ETH Zurich, Max Planck Society, EPFL, and University of Cambridge as well as laboratories like Bell Labs, AT&T Laboratories, Siemens, and NVIDIA. The symposium covers methodology adopted by groups working with benchmarks from projects like SPEC, datasets from initiatives such as ImageNet, Kaggle, and UCI Machine Learning Repository, and comparisons to analytical results from conferences such as SODA, STOC, FOCS, ICALP, and ESA.
The symposium originated in 1999 as a response to increased interest in empirical validation among communities represented by ACM, SIAM, IEEE Computer Society, European Research Council, and workshops connected to European Symposium on Algorithms. Early meetings featured contributors affiliated with University of Washington, Harvard University, Yale University, Columbia University, and University of Illinois Urbana-Champaign, with program committees including members from Tel Aviv University, Weizmann Institute of Science, University of Toronto, and University of Oxford. Over time ALENEX evolved alongside initiatives such as Algorithmica, Journal of Experimental Algorithmics, and programs from National Science Foundation, European Commission, and DARPA.
ALENEX addresses practical evaluation topics such as benchmarking, reproducibility, implementation engineering, and performance tuning in contexts linked to datasets from CERN, NASA, NOAA, and World Health Organization. The program includes experimental studies relating to algorithmic problems frequently researched in Computational Geometry, Graph Theory, Stringology, Combinatorial Optimization, and Machine Learning communities connected to conferences like SIGGRAPH, KDD, NeurIPS, and ACL. Papers often compare methods used in projects at Facebook AI Research, DeepMind, OpenAI, Twitter, and LinkedIn as well as algorithmic systems developed at Oracle Corporation and SAP SE.
ALENEX is organized by program and organizing committees drawn from universities such as University of Pennsylvania, University of Maryland, College Park, Brown University, and Dartmouth College and by professional societies like ACM Special Interest Group on Algorithms and Computation Theory and SIAM Activity Group on Discrete Mathematics. The format commonly includes invited talks by researchers affiliated with ETH Zurich, Princeton University, University of California, San Diego, and Columbia University, poster sessions showcasing work from graduate students at Stanford University, MIT, and University of Michigan, and tutorials coordinated with workshops tied to SODA and ESA.
Proceedings are typically published in collections associated with SIAM, ACM Digital Library, or in special issues of journals such as Journal of Experimental Algorithmics and Algorithmica, and are archived for citation alongside works from STOC and FOCS. Authors often release artifacts and code on platforms used by GitHub, Zenodo, FigShare, and institutional repositories at Harvard University, MIT Libraries, and University of California. The proceedings facilitate cross-references to benchmarks and datasets maintained by UCI Machine Learning Repository, Kaggle, and domain repositories from European Bioinformatics Institute and GenBank.
ALENEX acknowledges outstanding contributions with awards presented by committees including members from ACM, SIAM, IEEE, and funding agencies such as NSF and ERC. Recipients frequently hold positions at institutions like Carnegie Mellon University, University of Cambridge, ETH Zurich, and University of Toronto and have later received honors from bodies including Royal Society, National Academy of Sciences, and IEEE Fellow designation.
Notable ALENEX papers have influenced implementation practices in libraries and systems such as Boost (C++ Libraries), CGAL, Boost.Geometry, LLVM, and GCC, and informed software projects at Apache Software Foundation, Linux Foundation, PostgreSQL Global Development Group, and Mozilla Foundation. Research presented has been cited alongside foundational results from Dijkstra's algorithm, Kruskal's algorithm, Ford–Fulkerson algorithm, and has impacted applied work in domains represented by Google Maps, Waze, Spotify, and Dropbox. The empirical emphasis of ALENEX has promoted reproducibility norms echoed in initiatives like FAIR data principles, ReproZip, and practices adopted by journals including Nature, Science, and PNAS.