Difference between revisions of "Event: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
|Field=Algorithms
 
 
|Official Website=https://siam.org/conferences/cm/conference/soda20
 
|Official Website=https://siam.org/conferences/cm/conference/soda20
 
|Type=Conference
 
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'''31st Annual ACM/SIAM Symposium on Discrete Algorithms''' (SODA 2020)
 
'''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.''
 
''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.''

Revision as of 14:02, 24 August 2022

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.

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