Difference between revisions of "SODA 2020"

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|Start date=2020/01/05
 
|Start date=2020/01/05
 
|End date=2020/01/08
 
|End date=2020/01/08
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|Submission deadline=2019/07/05
 
|Homepage=https://www.siam.org/conferences/cm/conference/soda20
 
|Homepage=https://www.siam.org/conferences/cm/conference/soda20
 
|City=Salt Lake City
 
|City=Salt Lake City
 
|State=Utah
 
|State=Utah
 
|Country=USA
 
|Country=USA
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|Has host organization=Society for Industrial and Applied Mathematics
 
|has program chair=Shuchi Chawla
 
|has program chair=Shuchi Chawla
 
|has Keynote speaker=Sanjeev Arora, Monika Henzinger, Nike Sun, Stéphan Thomassé
 
|has Keynote speaker=Sanjeev Arora, Monika Henzinger, Nike Sun, Stéphan Thomassé
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'''31st Annual ACM/SIAM Symposium on Discrete Algorithms''' (SODA 2020)
 
'''31st Annual ACM/SIAM Symposium on Discrete Algorithms''' (SODA 2020)
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''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 06:26, 25 August 2020

SODA 2020
31st Annual ACM/SIAM Symposium on Discrete Algorithms
Event in series SODA
Dates 2020/01/05 (iCal) - 2020/01/08
Homepage: https://www.siam.org/conferences/cm/conference/soda20
Location
Location: Salt Lake City, Utah, USA
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Important dates
Submissions: 2019/07/05
Papers: Submitted 594 / Accepted 182 (30.6 %)
Committees
PC chairs: Shuchi Chawla
Keynote speaker: Sanjeev Arora, Monika Henzinger, Nike Sun, Stéphan Thomassé
Table of Contents


31st Annual ACM/SIAM Symposium on Discrete Algorithms (SODA 2020)

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.