We describe SharpSAT-TD, our submission to the unweighted and weighted tracks
of the Model Counting Competition in 2021-2023, which has won in total $6$
first places in different tracks of the competition. SharpSAT-TD is based on
SharpSAT [Thurley, SAT 2006], with the primary novel modification being the use
of tree decompositions in the variable selection heuristic as introduced by the
authors in [CP 2021]. Unlike the version of SharpSAT-TD evaluated in [CP 2021],
the current version that is available in this https URL
features also other significant modifications compared to the original
SharpSAT, for example, a new preprocessor.

SharpSAT-TD 是我们在 2021-2023 年的模型计数竞赛中的获奖作品，它基于 SharpSAT，主要的创新是使用了作者在 CP 2021 中介绍的变量选择启发式中的树分解方法，并且相比原始的 SharpSAT 还有其他重要的修改，例如新增了一个预处理器。

2021-2023 模型计数竞赛中的 SharpSAT-TD

SharpSAT-TD in Model Counting Competitions 2021-2023

A machine learning configuration refers to a combination of preprocessor,
learner, and hyperparameters. Given a set of configurations and a large dataset
randomly split into training and testing set, we study how to efficiently
select the best configuration with approximately the highest testing accuracy
when trained from the training set. To guarantee small accuracy loss, we
develop a solution using confidence interval (CI)-based progressive sampling
and pruning strategy. Compared to using full data to find the exact best
configuration, our solution achieves more than two orders of magnitude speedup,
while the returned top configuration has identical or close test accuracy.

研究了在给定一组配置和一个大的数据集随机划分为训练和测试集的情况下，如何在训练集上训练以获取近似最高测试精度的最佳配置，使用置信区间（CI）-based 渐进采样和剪枝策略，能够保证小的精度损失。与使用完整数据寻找精确最佳配置相比，我们的解决方案实现了超过两个数量级的速度提升，而返回的顶部配置具有相同或接近的测试准确性。