Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA):
This symposium focuses on research topics related to efficient algorithms and data structures for discrete problems. In addition to the design of such methods and structures, the scope also includes their use, performance analysis, and the mathematical problems related to their development or limita...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Philadelphia, [Pennsylvania]
SIAM Society for Industrial and Applied Mathematics
[2024]
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| Schlagworte: | |
| Online-Zugang: | DE-91 DE-20 DE-29 Volltext |
| Zusammenfassung: | This symposium focuses on research topics related to efficient algorithms and data structures for discrete problems. In addition to the design of such methods and structures, the scope also includes their use, performance analysis, and the mathematical problems related to their development or limitations. Performance analyses may be analytical or experimental and may address worst-case or expected-case performance. Studies can be theoretical or based on data sets that have arisen in practice and may address methodological issues involved in performance analysis. |
| Beschreibung: | The papers in this volume were presented at the Thirty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2024), held January 7-10, 2024, in Alexandria, Virginia. |
| Beschreibung: | 1 Online-Ressource (xx, 5360 Seiten) |
| ISBN: | 9781611977912 |
| DOI: | 10.1137/1.9781611977912 |
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| isbn | 9781611977912 |
| language | English |
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| spelling | ACM-SIAM SIAM Symposium on Discrete Algorithms 35. 2024 Alexandria, Virginia. Verfasser aut Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) editor(s): David P. Woodruff SODA24 SODA 24 SODA 2024 Philadelphia, [Pennsylvania] SIAM Society for Industrial and Applied Mathematics [2024] © 2024 1 Online-Ressource (xx, 5360 Seiten) txt rdacontent c rdamedia cr rdacarrier The papers in this volume were presented at the Thirty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2024), held January 7-10, 2024, in Alexandria, Virginia. This symposium focuses on research topics related to efficient algorithms and data structures for discrete problems. In addition to the design of such methods and structures, the scope also includes their use, performance analysis, and the mathematical problems related to their development or limitations. Performance analyses may be analytical or experimental and may address worst-case or expected-case performance. Studies can be theoretical or based on data sets that have arisen in practice and may address methodological issues involved in performance analysis. (DE-588)1071861417 Konferenzschrift gnd-content Woodruff, D. P. 1944- (DE-588)1031497390 edt https://doi.org/10.1137/1.9781611977912 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) |
| subject_GND | (DE-588)1071861417 |
| title | Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) |
| title_alt | SODA24 SODA 24 SODA 2024 |
| title_auth | Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) |
| title_exact_search | Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) |
| title_exact_search_txtP | Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) |
| title_full | Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) editor(s): David P. Woodruff |
| title_fullStr | Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) editor(s): David P. Woodruff |
| title_full_unstemmed | Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) editor(s): David P. Woodruff |
| title_short | Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) |
| title_sort | proceedings of the 2024 annual acm siam symposium on discrete algorithms soda |
| topic_facet | Konferenzschrift |
| url | https://doi.org/10.1137/1.9781611977912 |
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