.png){kind=icon}
返回
.png){kind=icon}
.png){kind=icon}
.png){kind=icon}
摘要
En 中文
Many proofs in discrete mathematics and theoretical computer science are based on the probabilistic method. To prove the existence of a good object, we pick a random object and show that it is bad with low probability. This method is effective, but the underlying probabilistic machinery can be daunting. Encoding arguments provide an alternative presentation in which probabilistic reasoning is encapsulated in a uniform encoding lemma. This lemma provides an upper bound on the probability of an event using the fact that a uniformly random choice from a set of size n cannot be encoded with fewer than log(2)n bits on average. With the lemma, the argument reduces to devising an encoding where bad objects have short codewords. In this expository article, we describe the basic method and provide a simple tutorial on how to use it. After that, we survey many applications to classic problems from discrete mathematics and computer science. We also give a generalization for the case of non-uniform distributions, as well as a rigorous justification for the use of non-integer codeword lengths in encoding arguments. These latter two results allow encoding arguments to be applied more widely and to produce tighter results.
Keyword:
Encoding arguments
entropy
incompressibility
analysis of algorithms
hash tables
random graphs
expanders
concentration inequalities
percolation theory
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
28
论文数:
2.4K
被引数:
3.5W
机构
引用论文
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
查看更多内容