一种基于变量熵求解约束满足问题的置信传播算法

在置信传播(belief propagation,BP)算法中,提出一种基于变量熵来挑选变量从而固定变量赋值的策略,用于求解一类具有增长定义域的随机约束满足问题.RB模型是一个具有增长定义域的随机约束满足问题的典型代表,已经严格证明它不仅存在精确的可满足性相变现象,而且可以生成难解实例.在RB模型上选取两组不同的参数进行数值实验.结果表明:在接近可满足性相变点时,BP引导的消去算法仍然可以非常有效地找到随机实例的解;不断增加问题的规模,算法的运行时间呈指数级增长;并且当控制参数(约束紧度)增加时,变量的平均自由度逐渐降低.     

约束可满足性中求解RB模型实例的算法综述

约束满足问题是人工智能领域中最基本的NP完全问题之一。多年来,随着约束满足问题的深入研究,国内外学者提出多种实例模型。其中,RB模型是一种能生成具有精确相变的增长域约束满足问题实例,其求解难度极具挑战性。为了寻找其求解的新型高效算法,促进约束可满足问题的RB模型求解算法领域的研究,首先从约束满足问题的模型发展、求解技术进行分析;其次,对各类求解RB模型实例算法进行梳理,将求解的算法文献划分为回溯启发式类、信息传播类和元启发式类相关改进算法,从算法原理、改进策略、收敛性和精确度等方面进行对比综述;最后给出求解RB模型实例算法的研究趋势和发展方向。     

不同紧度下约束满足问题的相变现象

提出了一个基于RB模型的随机约束满足问题即◢p◣-RB模型。该模型考虑了约束紧度的多样性,将所有约束按照不同的权重分成若干组,同一组具有相同的约束紧度,而相异组具有不同的约束紧度。用二阶矩方法严格证明了随着控制参数的不断增加,◢p◣-RB模型发生了精确的可满足性相变现象。数值实验表明该模型的有解概率经历了从1到0的突然转变,同时求解难度在相变区域达到高峰,表明该模型在相变区域能产生大量的难解实例。     

启发式回溯算法求解约束满足问题

针对一个典型的具有可变取值域的随机约束满足问题,提出了利用度启发式策略和最少约束值启发式策略来选择变量进行赋值的不完备回溯算法。该算法首先通过度启发式来确定待赋值变量的顺序,然后利用最少约束值启发式对选择的变量进行赋值,最后在有限时间内通过回溯得到变量的一组取值。用此算法对由RB模型生成的随机实例进行求解,实验结果表明,与经典的回溯算法相比,该算法具有显著的优越性。在控制参数(即约束紧度)进入相变区域时,该算法能在较短的时间内有效地找到实例的解。     

基于动态度的回溯算法求解大值域约束满足问题

针对一个具有精确可满足性相变现象的大值域随机约束满足问题,提出了两种启发式动态回溯算法,即基于动态度的ddeg-MAC（dynamic degree-maintaining arc consistency）回溯算法和基于值域与动态度比值的dom/ddeg-MAC（dom/dynamic degree-maintaining arc consistency）回溯算法。这两种算法分别基于ddeg和dom/ddeg挑选变量,利用维持弧相容（MAC）技术为挑选的变量进行赋值。当赋值无法进行时,再执行动态回溯修正变量的赋值。数值实验结果表明：在控制参数非常接近理论相变点时,算法仍然能够有效地找到问题的解。与经典回溯算法相比,这两种启发式动态回溯算法具有显著的优越性。     

可满足实例的归结复杂度

GRB模型是一种随机约束满足问题模型,此模型具有精确的可满足相变现象。针对实验中出现的GRB模型在相变区域产生的可满足实例都是难解的现象,利用子句宽度和归结复杂度的关系证明了GRB模型在相变点附近产生的可满足实例对于树型归结证明具有指数下界。因此从理论上证明了在相变区域产生的可满足实例对基于归结的算法是难解的。     

O（m^2）时间求解SAT问题的随机算法

传统的求解SAT问题的随机算法主要是对满足解进行搜索,在找不到满足解的情况下,则无法正确判断问题的可满足性。该文提出了两个时间复杂度为O（m^2）求解SAT问题的随机算法SatTestl和SatTest2,这里m为CNF公式中的子句数。这两个随机算法是通过对不满足解数的估计来判断SAT问题的可满足性,不同于传统的随机算法。其中第二个算法SatTest2在搜索满足解的同时又可以对不满足解数进行估计,是对传统随机算法的重要改进。试验结果表明,文中提出的算法对相变区域的难SAT实例有较好的求解能力。     

改进GSA算法在协同制造任务分配中的应用

针对网络化协同制造中的任务分配问题,建立了以制造任务完成时间、完成成本、产品工艺质量为目标的多目标优化模型,提出了模型求解的改进遗传模拟退火（Genetic Simulated Annealing,GSA）算法。建立了协同制造任务分配的层次结构模型,应用模糊层次分析法分析了时间、成本和工艺质量等因素在协同制造任务分配过程中的相对重要性。设计了优化模型求解的改进遗传模拟退火算法,并结合具体实例验证了算法的有效性和优越性。     

置信传播和模拟退火相结合求解约束满足问题

约束满足问题是人工智能领域的一个重要问题。针对一个具有精确相变现象和能产生大量难解实例的随机约束满足问题,提出了置信传播和模拟退火相结合的求解算法。这种算法先通过置信传播方程收敛后得到变量取值的边际概率分布,分别采用最大概率和最小分量熵的策略产生一组启发式的初始赋值,再用模拟退火对这组赋值进行修正。实验结果表明：该算法大大提高了初始赋值向最优解收敛的速度,表现出了显著优越于模拟退火算法的求解性能。     

值域增长约束满足问题的无回溯与随机行走策略的算法复杂性分析

值域增长的约束满足问题模型是计算复杂性理论中一类重要的实际问题模型,针对解决这类问题的算法研究仍然很少。通过研究RB模型这一典型的值域增长约束满足问题,发现当问题规模很大时,无回溯策略比随机行走策略更加有效。这与典型的值域确定的约束满足问题如SAT问题不同,是值域增长的约束满足问题所特有的性质。通过实验研究了两种策略的表现,并进一步对两种策略的表现进行了分析。     

Variable-Centered Consistency in Model RB

Model RB is a model of random constraint satisfaction problems, which exhibits exact satisfiability phase transition and many hard instances, both experimentally and theoretically. Benchmarks based on Model RB have been successfully used by various international algorithm competitions and many research papers. In a previous work, Xu and Li defined two notions called i-constraint assignment tuple and flawed i-constraint assignment tuple to show an exponential resolution complexity of Model RB. These two notions are similar to some kind of consistency in constraint satisfaction problems, but seem different from all kinds of consistency so far known in literatures. In this paper, we explicitly define this kind of consistency, called variable-centered consistency, and show an upper bound on a parameter in Model RB, such that up to this bound the typical instances of Model RB are variable-centered consistent.     

随机约束满足问题的回溯算法分析

提出一种新的随机CSP(constraint sa tisfaction problem)模型,并且通过研究搜索树的平均节点数,分析了回溯算法求解该模型 的平均复杂性.结果表明,这种模型能够生成难解的CSP实例,找到所有的解或证明无解所需的 平均节点数即随变量数的增加而指数增长.因此,该模型可以用来研究难解实例的性质和CSP 算法的性能等问题,从而有助于设计出更为高效的算法.     

Many hard examples in exact phase transitions

This paper analyzes the resolution complexity of two random constraint satisfaction problem (CSP) models (i.e. Model RB/RD) for which we can establish the existence of phase transitions and identify the threshold points exactly. By encoding CSPs into CNF formulas, it is proved that almost all instances of Model RB/RD have no tree-like resolution proofs of less than exponential size. Thus, we not only introduce new families of CSPs and CNF formulas hard to solve, which can be useful in the experimental evaluation of CSP and SAT algorithms, but also propose models with both many hard instances and exact phase transitions. Finally, conclusions are presented, as well as a detailed comparison of Model RB/RD with the Hamiltonian cycle problem and random 3-SAT, which, respectively, exhibit three different kinds of phase transition behavior in NP-complete problems.     

Spines of random constraint satisfaction problems: definition and connection with computational complexity

We study the connection between the order of phase transitions in combinatorial problems and the complexity of decision algorithms for such problems. We rigorously show that, for a class of random constraint satisfaction problems, a limited connection between the two phenomena indeed exists. Specifically, we extend the definition of the spine order parameter of Bollobás et al. [10] to random constraint satisfaction problems, rigorously showing that for such problems a discontinuity of the spine is associated with a 2^Ω(n) resolution complexity (and thus a 2^Ω(n) complexity of DPLL algorithms) on random instances. The two phenomena have a common underlying cause: the emergence of “large” (linear size) minimally unsatisfiable subformulas of a random formula at the satisfiability phase transition.We present several further results that add weight to the intuition that random constraint satisfaction problems with a sharp threshold and a continuous spine are “qualitatively similar to random 2-SAT”. Finally, we argue that it is the spine rather than the backbone parameter whose continuity has implications for the decision complexity of combinatorial problems, and we provide experimental evidence that the two parameters can behave in a different manner.AMS subject classification 68Q25, 82B27     

一类布尔方程组的可满足性阈值研究*

以一类布尔方程组形式的NP问题可满足性阈值估计为研究目的,通过将高斯消去算法与摘叶算法相结合的方法给出了一种求解该问题的完全算法,并通过不同参数条件下对大量随机实例进行数值实验得到了原问题可满足性阈值的算法估计值。所得研究结果不仅首次给出了该问题的可满足性阈值估计,而且可以作为相关启发式完全算法的设计依据。     

Threshold behaviors of a random constraint satisfaction problem with exact phase transitions

We consider a random constraint satisfaction problem named model RB, which exhibits a sharp satisfiability phase-transition phenomenon when the control parameters pass through the critical values denoted by rcr and pcr. Using finite-size scaling analysis, we bound the width of the transition region for finite problem size n, which might be the first rigorous study on the threshold behaviors of NP-complete problems.     

Maintenance scheduling problems as benchmarks for constraint algorithms

The paper focuses on evaluating constraint satisfaction search algorithms on application based random problem instances. The application we use is a well-studied problem in the electric power industry: optimally scheduling preventive maintenance of power generating units within a power plant. We show how these scheduling problems can be cast as constraint satisfaction problems and used to define the structure of randomly generated non-binary CSPs. The random problem instances are then used to evaluate several previously studied algorithms. The paper also demonstrates how constraint satisfaction can be used for optimization tasks. To find an optimal maintenance schedule, a series of CSPs are solved with successively tighter cost-bound constraints. We introduce and experiment with an “iterative learning” algorithm which records additional constraints uncovered during search. The constraints recorded during the solution of one instance with a certain cost-bound are used again on subsequent instances having tighter cost-bounds. Our results show that on a class of randomly generated maintenance scheduling problems, iterative learning reduces the time required to find a good schedule. This revised version was published online in June 2006 with corrections to the Cover Date.