论文标题
信心阈值神经潜水
Confidence Threshold Neural Diving
论文作者
论文摘要
在较短的时间内找到更好的可行解决方案是解决混合整数程序的组成部分。我们提出了一种基于神经潜水的事后方法，以更灵活地构建启发式方法。我们假设具有较高置信分数的变量更明确地包括在最佳解决方案中。对于我们的假设，我们提供了经验证据，表明置信阈值技术会产生部分解决方案，从而导致具有更好原始客观值的最终解决方案。我们的方法在Neurips 2021 ML4CO比赛中赢得了第二名。此外，我们的方法在竞争中显示了其他基于学习的方法中最佳分数。
Finding a better feasible solution in a shorter time is an integral part of solving Mixed Integer Programs. We present a post-hoc method based on Neural Diving to build heuristics more flexibly. We hypothesize that variables with higher confidence scores are more definite to be included in the optimal solution. For our hypothesis, we provide empirical evidence that confidence threshold technique produces partial solutions leading to final solutions with better primal objective values. Our method won 2nd place in the primal task on the NeurIPS 2021 ML4CO competition. Also, our method shows the best score among other learning-based methods in the competition.
