Curator 91 (talk | contribs) |
Curator 100 (talk | contribs) |
||
| (10 intermediate revisions by 3 users not shown) | |||
| Line 7: | Line 7: | ||
|Start Date=2019/01/06 | |Start Date=2019/01/06 | ||
|End Date=2019/01/09 | |End Date=2019/01/09 | ||
| − | |Field= | + | |Event Status=as scheduled |
| + | |Event Mode=on site | ||
| + | |City=San Diego | ||
| + | |Region=CA | ||
| + | |Country=Country:US | ||
| + | |Academic Field=Algorithm; Computer Science; Data Science | ||
|Official Website=https://www.siam.org/conferences/cm/conference/soda19 | |Official Website=https://www.siam.org/conferences/cm/conference/soda19 | ||
| + | |Submission Link=https://easychair.org/account/signin?l=187i3NKgs7Ick7xZO3wQOL# | ||
| + | |DOI=10.25798/k178-4153 | ||
|Type=Conference | |Type=Conference | ||
| − | + | |has program chair=Timothy Chan | |
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |has program chair=Timothy | ||
| − | |||
| − | |||
|has Proceedings DOI=: https://doi.org/10.1137/1.9781611975482.fm | |has Proceedings DOI=: https://doi.org/10.1137/1.9781611975482.fm | ||
|pageCreator=User:Curator 19 | |pageCreator=User:Curator 19 | ||
| Line 25: | Line 23: | ||
|contributionType=1 | |contributionType=1 | ||
}} | }} | ||
| + | {{Event Deadline | ||
| + | |Paper Deadline=2018/07/12 | ||
| + | |Submission Deadline=2018/07/05 | ||
| + | }} | ||
| + | {{Organizer | ||
| + | |Contributor Type=organization | ||
| + | |Organization=Society for Industrial and Applied Mathematics | ||
| + | }} | ||
| + | {{Event Metric | ||
| + | |Number Of Submitted Papers=591 | ||
| + | |Number Of Accepted Papers=184 | ||
| + | }} | ||
| + | {{S Event}} | ||
'''2019 Annual ACM-SIAM Symposium on Discrete Algorithms''' | '''2019 Annual ACM-SIAM Symposium on Discrete Algorithms''' | ||
''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.'' | ''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.'' | ||
Latest revision as of 11:50, 4 August 2023
The document "SODA 2019" was published on "2023-08-04T11:50:41" on the website "ConfIDent" under the URL https://confident-conference.org/index.php/Event:SODA 2019.
The document "SODA 2019" describes an event in the sense of a conference.
The document "SODA 2019" contains information about the event "SODA 2019" with start date "2019/01/06" and end date "2019/01/09".
The event "SODA 2019" is part of the event series identified by [[Event Series:SODA]]
Deadlines
|
||
| Submission |
|
||
| Paper |
Metrics
Submitted Papers
591
Accepted Papers
184
Venue
San Diego, CA, United States of America
Warning: Venue is missing. The map might not show the exact location.
2019 Annual ACM-SIAM Symposium on Discrete Algorithms
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.
