Satisfiability solvers targeting industrial instances are currently almost always based on conflict-driven clause learning (CDCL) . This technique can successfully solve very large instances. Yet on small, hard problems lookahead solvers  often perform better by applying much more reasoning in each search node and then recursively splitting the search space until a solution is found. The cube-and-conquer (CC) approach  has shown that the two techniques can be combined, resulting in better performance particularly for very hard instances. The key insight is that lookahead solvers can be used to partition the search space into subproblems (called cubes) that are easy for a CDCL solver to solve. By first partitioning (cube phase) and then solving each cube (conquer phase), some instances can be solved within hours rather than days. This cubeand-conquer approach, particularly the conquer phase, is also easy to parallelize. The challenge to make this technique work in practice lies in developing effective heuristics to determine when to stop partitioning and start solving. The current heuristics already give strong results for very hard instances, but are far
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