Learning Rate Based Branching Heuristic for SAT Solvers
Liang J.H., Ganesh V., Poupart P., Czarnecki K. (2016) Learning Rate Based Branching Heuristic for SAT Solvers. In: Creignou N., Le Berre D. (eds) Theory and Applications of Satisfiability Testing – SAT 2016. SAT 2016. Lecture Notes in Computer Science, vol 9710. Springer, Cham
Abstract
| In this paper, we propose a framework for viewing solver branching heuristics as optimization algorithms where the objective is to maximize the learning rate, defined as the propensity for variables to generate learnt clauses. By viewing online variable selection in SAT solvers as an optimization problem, we can leverage a wide variety of optimization algorithms, especially from machine learning, to design effective branching heuristics. 译文：通过将SAT求解器中的在线变量选择视为一个优化问题，我们可以利用各种优化算法，特别是机器学习，来设计有效的分支启发式。 In particular, we model the variable selection optimization problem as an online multi-armed bandit, a special-case of reinforcement learning, to learn branching variables such that the learning rate of the solver is maximized. We develop a branching heuristic that we call learning rate branching or LRB, based on a well-known multi-armed bandit algorithm called exponential recency weighted average and implement it as part of MiniSat and CryptoMiniSat. LRB与multi-armed bandit algorithm的关系：使用指数移动权值平均算法实现LRB。 We upgrade the LRB technique with two additional novel ideas to improve the learning rate by accounting for reason side rate and exploiting locality. The resulting LRB branching heuristic is shown to be faster than the VSIDS and conflict history-based (CHB) branching heuristics on 1975 application and hard combinatorial instances from 2009 to 2014 SAT Competitions. We also show that CryptoMiniSat with LRB solves more instances than the one with VSIDS. These experiments show that LRB improves on state-of-the-art. |
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Keywords
Learning Rate Slot Machine Implication Graph CDCL Solver Clause Learning
1 Introduction
| branching heuristic (and its variants) | 文献 |
| VSIDS 被提出在2001年， | 【24】 |