论文标题
快速的超级参数调整iSing机器
Fast Hyperparameter Tuning for Ising Machines
论文作者
论文摘要
在本文中，我们提出了一种新型技术来加速ISING机器高参数调整。首先，我们定义了Ising机器性能，并解释了有关此性能定义的超参数调整的目标。其次，我们比较了众所周知的高参数调谐技术，即在不同组合优化问题上的随机抽样和树结构的parzen估计量（TPE）。第三，我们提出了一种用于TPE的新收敛加速方法，我们称之为“ FastConvergence”。它旨在限制所需的TPE试验的数量，以达到最佳性能的超参数值组合。我们将FastConvergence与前面提到的众所周知的高参数调谐技术进行了比较，以显示其有效性。对于实验，众所周知的旅行推销员问题（TSP）和二次分配问题（QAP）实例被用作输入。使用的ISING机器是Fujitsu的第三代数字发火器（DA）。结果表明，在大多数情况下，FastConvergence可以在不到一半的试验次数之内获得与TPE相似的结果。
In this paper, we propose a novel technique to accelerate Ising machines hyperparameter tuning. Firstly, we define Ising machine performance and explain the goal of hyperparameter tuning in regard to this performance definition. Secondly, we compare well-known hyperparameter tuning techniques, namely random sampling and Tree-structured Parzen Estimator (TPE) on different combinatorial optimization problems. Thirdly, we propose a new convergence acceleration method for TPE which we call "FastConvergence".It aims at limiting the number of required TPE trials to reach best performing hyperparameter values combination. We compare FastConvergence to previously mentioned well-known hyperparameter tuning techniques to show its effectiveness. For experiments, well-known Travel Salesman Problem (TSP) and Quadratic Assignment Problem (QAP) instances are used as input. The Ising machine used is Fujitsu's third generation Digital Annealer (DA). Results show, in most cases, FastConvergence can reach similar results to TPE alone within less than half the number of trials.
