Learning Variable Ordering Heuristics with Multi-Armed Bandits and Restarts

In constraint-based applications, the user is often required to be an expert as, for a given problem instance, many parameters of the used solver must be manually tuned to improve its efficiency. Clearly, this background knowledge burdens the spread of constraint programming technology to non-expert users. In order to alleviate this issue, the idea of "autonomous" constraint solving is to adjust the solver parameters and to efficiently handle any problem instance without manual tuning. Notably, the choice of the variable ordering heuristic can lead to drastically different performances. A key question arises then: how can we find the best variable ordering heuristic for a problem instance, given a set of available heuristics provided by the solver? To answer this question, we propose an algorithmic framework that combines multi-armed bandits and restarts. Each candidate heuristic is viewed as an arm, and the framework learns to estimate the best heuristic using a multi-armed bandit algorithm. The common mechanism of restarts is used to provide feedback for reinforcing the bandit algorithm. Based on a thorough experimental evaluation, we demonstrate that this framework is able to find the best heuristic for most problem instances; notably, it outperforms the state-of-the-art in terms of time and solved instances.