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Understanding VSIDS Branching Heuristics in Conflict-Driven Clause-Learning SAT Solvers

机译:在冲突驱动子句学习SAT解算器中了解VSIDS分支启发法

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Conflict-Driven Clause-Learning (CDCL) SAT solvers crucially depend on the Variable State Independent Decaying Sum (VSIDS) branching heuristic for their performance. Although VSIDS was proposed nearly fifteen years ago, and many other branching heuristics for SAT solving have since been proposed, VSIDS remains one of the most effective branching heuristics. Despite its widespread use and repeated attempts to understand it, this additive bumping and multiplicative decay branching heuristic has remained an enigma. In this paper, we advance our understanding of VSIDS by answering the following key questions. The first question we pose is "what is special about the class of variables that VSIDS chooses to additively bump?" In answering this question we showed that VSIDS overwhelmingly picks, bumps, and learns bridge variables, defined as the variables that connect distinct communities in the community structure of SAT instances. This is surprising since VSIDS was invented more than a decade before the link between community structure and SAT solver performance was discovered. Additionally, we show that VSIDS viewed as a ranking function correlates strongly with temporal graph centrality measures. Putting these two findings together, we conclude that VSIDS picks high-centrality bridge variables. The second question we pose is "what role does multiplicative decay play in making VSIDS so effective?" We show that the multiplicative decay behaves like an exponential moving average (EMA) that favors variables that persistently occur in conflicts (the signal) over variables that occur intermittently (the noise). The third question we pose is "whether VSIDS is temporally and spatially focused." We show that VSIDS disproportionately picks variables from a few communities unlike, say, the random branching heuristic. We put these findings together to invent a new adaptive VSIDS branching heuristic that solves more instances than one of the best-known VSIDS variants over the SAT Competition 2013 benchmarks.
机译:冲突驱动子句学习(CDCL)SAT求解器的性能关键取决于可变状态独立衰变和(VSIDS)分支启发式算法。尽管VSIDS是在15年前提出的,并且此后又提出了许多其他的求解SAT的分支启发式方法,但VSIDS仍然是最有效的分支启发式方法之一。尽管它得到了广泛的使用和反复的理解,但是这种累加的碰撞和乘法衰变分支启发法仍然是一个谜。在本文中,我们通过回答以下关键问题来加深对VSIDS的理解。我们提出的第一个问题是“ VSIDS选择累加碰撞的变量类别有何特别之处?”在回答这个问题时,我们表明VSIDS压倒性地拾取,颠簸和学习桥梁变量,桥梁变量定义为在SAT实例社区结构中连接不同社区的变量。这是令人惊讶的,因为在发现社区结构和SAT解算器性能之间的联系之前,发明了VSIDS已有十多年了。此外,我们表明,被视为排名函数的VSIDS与时间图中心度度量密切相关。综合这两个发现,我们得出结论,VSIDS选择了高中心桥梁变量。我们提出的第二个问题是“乘法衰减在使VSIDS如此有效方面起什么作用?”我们显示出乘法衰减的行为就像指数移动平均线(EMA),它比在间歇性发生的变量(噪声)更偏向于在冲突中持续发生的变量(信号)。我们提出的第三个问题是“ VSIDS是否在时间和空间上受到关注”。我们证明,VSIDS不成比例地从一些社区中选择变量,这与随机分支启发法不同。我们将这些发现汇总在一起,发明了一种新的自适应VSIDS分支启发法,该算法比SAT Competition 2013基准中最知名的VSIDS变体之一可以解决更多实例。

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