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Applications of Algorithmic Metatheorems to Space Complexity and Parallelism (Invited Talk)

机译:算法Metatheorems对空间复杂性和平行度的应用(邀请谈话)

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Algorithmic metatheorems state that if a problem can be described in a certain logic and the inputs are structured in a certain way, then the problem can be solved with a certain amount of resources. As an example, by Courcelle's Theorem all monadic second-order ("in a certain logic") properties of graphs of bounded tree width ("structured in a certain way") can be solved in linear time ("with a certain amount of resources"). Such theorems have become a valuable tool in algorithmics: If a problem happens to have the right structure and can be described in the right logic, they immediately yield a (typically tight) upper bound on the time complexity of the problem. Perhaps even more importantly, several complex algorithms rely on algorithmic metatheorems internally to solve subproblems, which considerably broadens the range of applications of these theorems.
机译:算法Metatheorems状态,如果在某个逻辑中可以描述问题,并且输入以某种方式构造出输入,则可以用一定量的资源来解决问题。例如,通过阵列定理的所有Monadic二阶(“在某种逻辑”)的属性可以在线性时间(“以某种方式构造”图形)的属性(“具有一定量的资源“)。这些定理已成为算法中的有价值的工具:如果出现问题以具有正确的结构并且可以在右侧逻辑中描述,它们立即产生(通常是紧密的)上限的问题的时间复杂度。也许更重要的是,几种复杂算法依赖于内部算法的Metatheorems来解决子问题,这大大拓宽了这些定理的应用范围。

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