奇偶排序二元决策图中变量的随机排序

P. Savický
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引用次数: 1

摘要

有序二元决策图(obdd)是一种表示布尔函数的模型。还有一种更强大的变体称为奇偶obdd。在这两种模型中,给定函数表示的大小取决于所选择的变量顺序。众所周知,有些函数几乎所有变量的排序都会产生多项式大小的OBDD,但也有一些特殊的排序,其大小是指数级的。我们证明了对于奇偶obdd,随机排序的大小和最差排序的大小是多项式相关的。更确切地说,对于每一个大于0的λ,都存在一个大于0的数c,使得以下成立。如果一个有n个变量的布尔函数f使得变量的随机排序产生一个最大为s的奇偶OBDD,且概率至少为λ,其中s≥n,则变量的每次排序产生一个最大为sc的奇偶OBDD。©2000 John Wiley & Sons, Inc。随机结构。Alg。科学通报,16 (6):393 - 393,2000
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On random orderings of variables for parity ordered binary decision diagrams
Ordered binary decision diagrams (OBDDs) are a model for representing Boolean functions. There is also a more powerful variant called parity OBDDs. The size of the representation of a given function depends in both these models on the chosen ordering of the variables. It is known that there are functions such that almost all orderings of their variables yield an OBDD of polynomial size, but there are also some exceptional orderings, for which the size is exponential. We prove that for parity OBDDs, the size for a random ordering and the size for the worst ordering are polynomially related. More exactly, for every ϵ>0 there is a number c>0 such that the following holds. If a Boolean function f of n variables is such that a random ordering of the variables yields a parity OBDD for f of size at most s with probability at least ϵ, where s≥n, then every ordering of the variables yields a parity OBDD for f of size at most sc. © 2000 John Wiley & Sons, Inc. Random Struct. Alg., 16: 233–239, 2000
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