SODA 2021: ACM-SIAM Symposium on Discrete Algorithms 征稿
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2020-10-24
Society for Industrial and Applied Mathematics
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July 13, 2020
4:59 p.m. Eastern Time: Short Abstract Submission and Paper Registration
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November 9, 2020
We are currently accepting applications. Awards will be applied to a waiver of registration fees. Please do not make travel arrangements. This conference will be held in virtual format.
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. Algorithm Engineering and Experiments (ALENEX20), Symposium on Simplicity in Algorithms (SOSA20), and SIAM-ACM Symposium on Algorithmic Principles of Computer Systems (APOCS20) will take place at the same location. SODA is jointly sponsored by the SIAM Activity Group on Discrete Mathematics and the ACM Special Interest Group on Algorithms and Computation Theory. Included Themes Aspects of combinatorics and discrete mathematics, such as: Combinatorial structures Discrete optimization Discrete probability Finite metric spaces Graph theory Mathematical programming Random structures Topological problems Core topics in discrete algorithms, such as: Algorithm analysis Data structures Experimental algorithmics Algorithmic aspects of other areas of computer science, such as: Combinatorial scientific computing Communication networks and the Internet Computational geometry and topology Computer graphics and computer vision Computer systems Cryptography and security Databases and information retrieval Data compression Data privacy Distributed and parallel computing Game theory and mechanism design Machine learning Quantum computing