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arXiv 1302.3741cs.LOcs.CC
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单调多项式系统上牛顿法的上界及概率单计数器自动机的P-时间模型检验

Upper bounds for Newton's method on monotone polynomial systems, and P-time model checking of probabilistic one-counter automata

Alistair Stewart, Kousha Etessami, Mihalis Yannakakis

摘要:

各类无穷状态递归概率系统分析和模型检验的中心计算问题(包括拟生灭过程、多类型分支过程、随机上下文无关文法、概率下推自动机和递归马尔可夫链)是{ \em终止概率}的计算,并且计算这些概率又归结为计算对应的{ \em单调多项式系统}(MPS)的方程的{ \em最小不动点}(LFP)解,表示为x=P(x)。Etessami和Yannakakis证明了牛顿方法的分解变体对于任何具有非负解的MPS单调收敛于LFP解。随后,Esparza,Kiefer和Luttenberger得到了牛顿方法对某些类型MPS的收敛速度的上界。最近,更好的上限已获得特殊类的MPS。然而,在本文之前,对于任意(不一定是强连接的)MPS,牛顿方法的收敛速度作为输入MPS的编码大小|P|的函数,x=P(x),根本没有上界。在本文中,我们提供了最坏情况下的上界,作为输入编码大小|P|和Σ> 0的函数,对于具有LFP解q^* 的任何MPS,分解牛顿法(即使有舍入)在q^* 的加性误差Σ> 0内收敛所需的迭代次数。我们的上界基本上是最优的几个重要参数。使用我们的上界,并建立在以前的工作,我们获得了第一个P-时间算法(在标准图灵计算模型)的定量模型检验,在所需的精度,离散时间QBD和(等效)概率1-计数器自动机,相对于任何(固定)ω-正则或LTL属性。

英文摘要:

A central computational problem for analyzing and model checking various classes of infinite-state recursive probabilistic systems (including quasi-birth-death processes, multi-type branching processes, stochastic context-free grammars, probabilistic pushdown automata and recursive Markov chains) is the computation of {\em termination probabilities}, and computing these probabilities in turn boils down to computing the {\em least fixed point} (LFP) solution of a corresponding {\em monotone polynomial system} (MPS) of equations, denoted x=P(x). It was shown by Etessami & Yannakakis that a decomposed variant of Newton's method converges monotonically to the LFP solution for any MPS that has a non-negative solution. Subsequently, Esparza, Kiefer, & Luttenberger obtained upper bounds on the convergence rate of Newton's method for certain classes of MPSs. More recently, better upper bounds have been obtained for special classes of MPSs. However, prior to this paper, for arbitrary (not necessarily strongly-connected) MPSs, no upper bounds at all were known on the convergence rate of Newton's method as a function of the encoding size |P| of the input MPS, x=P(x). In this paper we provide worst-case upper bounds, as a function of both the input encoding size |P|, and epsilon > 0, on the number of iterations required for decomposed Newton's method (even with rounding) to converge within additive error epsilon > 0 of q^*, for any MPS with LFP solution q^*. Our upper bounds are essentially optimal in terms of several important parameters. Using our upper bounds, and building on prior work, we obtain the first P-time algorithm (in the standard Turing model of computation) for quantitative model checking, to within desired precision, of discrete-time QBDs and (equivalently) probabilistic 1-counter automata, with respect to any (fixed) omega-regular or LTL property.

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