可满足性求解技术研究

求解公式的可满足性在诸如形式化验证、电子设计自动化与人工智能等众多领域中都具有非常重要的理论与应用价值,成为近年来的研究热点。本文针对命题公式与一阶公式的可满足性问题,重点介绍了布尔可满足性与可满足性模理论求解技术的基本原理,并且根据算法的类型进行分类阐述,分析了各种算法的优缺点。最后,讨论了目前面临的主要挑战,对今后的研究方向进行了展望。     

基于布尔可满足性的组合电路ATPG算法

布尔可满足性被深入研究并广泛应用于电子设计自动化等领域。该文提出了一种基于布尔可满足性的组合电路ATPG改进算法。在采用当前最新布尔可满足性求解程序加速策略的基础上,比如冲突驱动训练、冲突导向回跳和重启动技术等,引入电路结构信息来实现基于结构的分支决策。通过新增的电路结构信息层,布尔可满足性求解程序只需稍加修改,就能利用和及时更新此信息。最后给出的实验结果表明了算法的可行性和有效性。     

基于布尔可满足性的电路设计错误诊断算法

提出了一种组合电路设计错误诊断算法,该算法结合传统基于模拟的方法和可满足性问题求解技术,在不依赖于故障模型的条件下实现对电路逻辑错误的诊断定位．提出了基于布尔可满足性的增量式电路诊断方法,通过对可满足解依据电路结构信息筛选分级,提高了多错误诊断定位的分辨率和准确性;并提出多项启发式方法,避免了大量不必要的操作,使算法在时间和内存上保持有效性．实验结果表明,利用形式验证的技术来导向模拟的过程,抓住了高复杂度的多错误定位问题的特征,提高了电路错误诊断的效率．     

Satisfiability Testing of Boolean Combinations of Pseudo-Boolean Constraints using Local-search Techniques

Some search problems are most directly specified by conjunctions of (sets of) disjunctions of pseudo-Boolean (PB) constraints. We study a logic PL ^PB whose formulas are of such form, and design local-search methods to compute models of PL ^PB theories. In our approach we view a PL ^PB theory T as a data structure, a concise representation of a certain propositional conjunctive normal form (CNF) theory cl(T) logically equivalent to T. The key idea is an observation that parameters needed by local-search algorithms for CNF theories, such as walksat, can be estimated on the basis of T without the need to compute cl(T) explicitly. We compare our methods to a local-search algorithm wsat(oip). The experiments demonstrate that our approach performs better. In order for wsat(oip) to handle arbitrary PL ^PB theories, it is necessary to represent disjunctions of PB constraints by sets of PB constraints, which often increases the size of the theory dramatically. A better performance of our method underscores the importance of developing solvers that work directly on PL ^PB theories. This paper combines and extends results included in conference papers [14, 15].     

可满足性问题的一种DNA表面计算模型

可满足性问题的一种DNA表面计算模型是一种特殊的DNA计算方法,该模型是采用荧光标记的策略和荧光猝灭技术,通过观察荧光灭光情况排除非解,从而有效的解决可满足性问题(SAT).该模型方法具有错误率低、编码简单、读取方便等很好的性能,能够大大减少实验过程中的错差.     

Relaxations of the Satisfiability Problem Using Semidefinite Programming

We derive a semidefinite relaxation of the satisfiability (SAT) problem and discuss its strength. We give both the primal and dual formulation of the relaxation. The primal formulation is an eigenvalue optimization problem, while the dual formulation is a semidefinite feasibility problem. We show that using the relaxation, a proof of the unsatisfiability of the notorious pigeonhole and mutilated chessboard problems can be computed in polynomial time. As a byproduct we find a new `sandwich" theorem that is similar to the sandwich theorem for Lovász' -function. Furthermore, the semidefinite relaxation gives a certificate of (un)satisfiability for 2SAT problems in polynomial time. By adding an objective function to the dual formulation, a specific class of polynomially solvable 3SAT instances can be identified. We conclude with discussing how the relaxation can be used to solve more general SAT problems and with some empirical observations.     

DNA自组装的可满足性问题模型

DNA自组装技术在DNA计算和纳米技术领域都发挥着极其重要的作用,许多小规模NP完全问题都可以通过自组装模型得以解决.文中以可满足问题为模型,通过构造范式中变量的特殊补链,使其与初始数据库中初始DNA链发生杂交反应,形成发夹结构,利用形成发夹结构的DNA链与没形成发夹结构的DNA链长度不同的特点,通过凝胶电泳将这些带发夹的DNA链提取出来;然后加入与这些特殊补链完全互补的DNA链,在一定温度下,通过碱基互补配对原则,发夹结构又将被重新打开.该模型充分利用了DNA分子间的自组装能力,在计算过程中只需要用到凝胶电泳操作,在一定程度上大大减少了因生物操作过多而引起的各种实验误差.     

PSL可满足问题的计算复杂度

PSL是一种用于描述并行系统的属性规约语言,包括线性时序逻辑FL和分支时序逻辑OBE两部分。由于OBE就是CTL,因此论文重点研究FL逻辑。理论上已证明许多难解的问题都可多项式变换为“可满足性”问题,“可满足性”问题是研究时序逻辑的核心问题之一,并已成为程序验证的一种有力工具;而计算复杂度是“可满足性”问题需要解决的最深刻的方向之一,其研究意义在于它可作为解决一类问题的难度的标准。文中在利用“铺砖模型”基础上,推导并得出FL的“可满足性”问题的计算复杂度为EXPSPACE—hard,这对正确评价解决该问题的各种算法的效率,进而确定对已有算法的改进余地具有重要的指导意义。     

基于消息传递关系网络的布尔可满足性预测

布尔可满足性求解能够验证的问题规模通常受限, 因此, 如何高精度地预测布尔可满足性问题的可满足性是一个重要研究问题, 也是一项具有挑战性的工作.相关研究工作一般使用由文字节点和子句节点组成的图来表示布尔可满足性问题的结构, 但是这种表征方法缺少了变量、子句之间的重要关系信息.在我们的方法中, 通过将原始布尔可满足性问题实例表征为多关系异构图的方式来表达变量和句子之间的关系, 并设计使用消息传递关系网络模型来捕获实例的关系信息, 提取了更多的结构特征.结果表明, 该模型在预测精度、泛化能力和资源需求等方面均优于现有模型, 对所选数据集的平均预测精度为81%.该模型在小规模问题(变量数为100)上训练, 在大规模数据集上预测的平均预测精度达到了80.8%.同时, 该模型对随机生成的非均匀随机问题的预测精度达到99%, 这意味着它学习了预测可满足性的重要特征.此外, 模型预测所花费的时间随着问题规模的增大也只是呈线性增长. 总结而言, 本文基于关系消息传递网络提出了一个预测精度更高, 泛化能力更好的布尔可满足性预测方法.     

Configuration landscape analysis and backbone guided local search.: Part I: Satisfiability and maximum satisfiability

Boolean satisfiability (SAT) and maximum satisfiability (Max-SAT) are difficult combinatorial problems that have many important real-world applications. In this paper, we first investigate the configuration landscapes of local minima reached by the WalkSAT local search algorithm, one of the most effective algorithms for SAT. A configuration landscape of a set of local minima is their distribution in terms of quality and structural differences relative to an optimal or a reference solution. Our experimental results show that local minima from WalkSAT form large clusters, and their configuration landscapes constitute big valleys, in that high quality local minima typically share large partial structures with optimal solutions. Inspired by this insight into WalkSAT and the previous research on phase transitions and backbones of combinatorial problems, we propose and develop a novel method that exploits the configuration landscapes of such local minima. The new method, termed as backbone-guided search, can be embedded in a local search algorithm, such as WalkSAT, to improve its performance. Our experimental results show that backbone-guided local search is effective on overconstrained random Max-SAT instances. Moreover, on large problem instances from a SAT library (SATLIB), the backbone guided WalkSAT algorithm finds satisfiable solutions more often than WalkSAT on SAT problem instances, and obtains better solutions than WalkSAT on Max-SAT problem instances, improving solution quality by 20% on average.     

Stochastic Boolean Satisfiability

Satisfiability problems and probabilistic models are core topics of artificial intelligence and computer science. This paper looks at the rich intersection between these two areas, opening the door for the use of satisfiability approaches in probabilistic domains. The paper examines a generic stochastic satisfiability problem, SSAT, which can function for probabilistic domains as SAT does for deterministic domains. It shows the connection between SSAT and well-studied problems in belief network inference and planning under uncertainty, and defines algorithms, both systematic and stochastic, for solving SSAT instances. These algorithms are validated on random SSAT formulae generated under the fixed-clause model. In spite of the large complexity gap between SSAT (PSPACE) and SAT (NP), the paper suggests that much of what we have learned about SAT transfers to the probabilistic domain.     

在形式验证和ATPG中的布尔可满足性问题

介绍布尔可满足性(SAT)求解程序在测试向量自动生成、符号模型检查、组合等价性检查和RTL电路设计验证等电子设计自动化领域中的应用．着重阐述如何在算法中有机地结合电路拓扑结构及其与特定应用相关的信息，以便提高问题求解效率．最后给出下一步可能的研究方向。     

可满足性问题中信念传播算法的收敛性分析

信念传播算法是基于因子图模型的消息传递算法,通过图中的边,将消息从一个结点传递给另一个结点,以高概率地确定部分变量的取值,这种方法被实验证明在求解可满足性问题时非常有效.然而,目前还未对其有效性从理论角度给予解释.通过对信念传播算法的收敛性分析,试图从理论上解释算法的有效性.在信息传播算法的信息迭代方程中,参数的取值范...     

采用二元CSP引擎求解RTL数据通路的可满足性

针对寄存器传输级(RTL)验证和测试过程中非常重要的数据通路可满足性求解问题,提出一种基于二元约束满足问题(CSP)的求解方法,包括数据通路提取、二元CSP建模和搜索求解3个步骤.数据通路提取通过对接口布尔变量和某些字变量赋值,为各个数据通路器件建立环境;二元CSP建模则根据该环境和各个数据通路器件的功能,将数据通路的可满足性问题转化为二元CSP描述;该二元CSP问题的描述被送入到二元CSP引擎,并采用冲突引导的回跳搜索策略进行求解,获得有解的例证或无解的判定.实验结果表明,即使在没有采取很多优化策略的条件下,该方法仍有较好的性能,并优于基于线性规划(LP)的求解方法.     

RTL验证中的混合可满足性求解

RTL混合可满足性求解方法分为基于可满足性模理论(SMT)和基于电路结构搜索两大类.前者主要使用逻辑推理的方法,目前已在处理器验证中得到了广泛的应用,主要得益于SMT支持用于描述验证条件的基础理论;后者能够充分地利用电路中的约束信息,因而求解效率较高.介绍了每一大类中的典型研究及其所采用的重要策略,以及RTL可满足性求解方面的研究进展.     

可满足问题的分子信标计算模型

分子信标（Molecular Beacon）是一种发夹状的荧光探针,它可以特异地和那些与分子信标的环（Loop）互补的核酸靶序列杂交,具有单个碱基错配的检测能力．肽核酸（Peptide Nucleic Acid）是人工合成的核酸（DNA）的类似物．PNA骨架为酰胺键,与DNA补链杂交更稳定,可以阻止聚合酶延伸反应．文中将可满足问题的约束变量编码于分子信标的环部识别区,通过分子信标与使得给定范式为真的变量的PNA补链杂交,再利用PNA链可以阻止聚合酶延伸反应的性质,用限制性内切酶EcoRI降解对应于非解的分子信标,最后通过加热表面使分子信标构形发生变化,产生荧光读解．提出的可满足问题的分子信标计算模型具有可靠性高、无需观察和记录计算的中间结果、读解简单等优点．     

Penalty Formulations and Trap-Avoidance Strategies for Solving Hard Satisfiability Problems

In this paper we study the solution of SAT problems formulated as discrete decision and discrete constrained optimization problems. Constrained formulations are better than traditional unconstrained formulations because violated constraints may provide additional forces to lead a search towards a satisfiable assignment. We summarize the theory of extended saddle points in penalty formulations for solving discrete constrained optimization problems and the associated discrete penalty method (DPM). We then examine various formulations of the objective function, choices of neighborhood in DPM, strategies for updating penalties, and heuristics for avoiding traps. Experimental evaluations on hard benchmark instances pinpoint that traps contribute significantly to the inefficiency of DPM and force a trajectory to repeatedly visit the same set of or nearby points in the original variable space. To address this issue, we propose and study two trap-avoidance strategies. The first strategy adds extra penalties on unsatisfied clauses inside a trap, leading to very large penalties for unsatisfied clauses that are trapped more often and making these clauses more likely to be satisfied in the future. The second strategy stores information on points visited before, whether inside traps or not, and avoids visiting points that are close to points visited before. It can be implemented by modifying the penalty function in such a way that, if a trajectory gets close to points visited before, an extra penalty will take effect and force the trajectory to a new region. It specializes to the first strategy because traps are special cases of points visited before. Finally, we show experimental results on evaluating benchmarks in the DIMACS and SATLIB archives and compare our results with existing results on GSAT, WalkSAT, LSDL, and Grasp. The results demonstrate that DPM with trap avoidance is robust as well as effective for solving hard SAT problems.     

Approximating Satisfiable Satisfiability Problems

We study the approximability of the Maximum Satisfiability Problem (MAX SAT) and of the boolean k -ary Constraint Satisfaction Problem (MAX k CSP) restricted to satisfiable instances. For both problems we improve on the performance ratios of known algorithms for the unrestricted case. Our approximation for satisfiable MAX 3CSP instances is better than any possible approximation for the unrestricted version of the problem (unless P=NP). This result implies that the requirement of perfect completeness weakens the acceptance power of non-adaptive PCP verifiers that read 3 bits. We also present the first non-trivial results about PCP classes defined in terms of free bits that collapse to P. Received August 1997; revised February 1999.     

Symbolic Techniques in Satisfiability Solving

Recent work has shown how to use binary decision diagrams for satisfiability solving. The idea of this approach, which we call symbolic quantifier elimination, is to view an instance of propositional satisfiability as an existentially quantified proposition formula. Satisfiability solving then amounts to quantifier elimination; once all quantifiers have been eliminated, we are left with either 1 or 0. Our goal in this work is to study the effectiveness of symbolic quantifier elimination as an approach to satisfiability solving. To that end, we conduct a direct comparison with the DPLL-based ZChaff, as well as evaluate a variety of optimization techniques for the symbolic approach. In comparing the symbolic approach to ZChaff, we evaluate scalability across a variety of classes of formulas. We find that no approach dominates across all classes. While ZChaff dominates for many classes of formulas, the symbolic approach is superior for other classes of formulas. Once we have demonstrated the viability of the symbolic approach, we focus on optimization techniques for this approach. We study techniques from constraint satisfaction for finding a good plan for performing the symbolic operations of conjunction and of existential quantification. We also study various variable-ordering heuristics, finding that while no heuristic seems to dominate across all classes of formulas, the maximum-cardinality search heuristic seems to offer the best overall performance. ★A preliminary version of the paper was presented in SAT'04. Supported in part by NSF grants CCR-9988322, CCR-0124077, CCR-0311326, IIS-9908435, IIS-9978135, EIA-0086264, ANI-0216467, and by BSF grant 9800096.