论文标题
使用ISING机器和分解机的整数变量问题的黑盒优化
Black-box optimization for integer-variable problems using Ising machines and factorization machines
论文作者
论文摘要
黑盒优化在许多应用中具有潜力，例如在实验设计中的机器学习和优化中的超参数优化。 Ising机器对二进制优化问题很有用，因为变量可以由Ising机器的单个二进制变量表示。但是，使用ISING机器的常规方法无法处理具有非二进制值的黑盒优化问题。为了克服这一限制，我们通过与三种不同的整数编码方法合作，通过使用ISING/退火计算机和分解计算机来提出一种用于整数变化的黑盒优化问题的方法。我们的方法的性能是使用不同的编码方法来评估的，该方法是在最稳定状态下计算氢分子能量的简单问题。提出的方法可以使用任何整数编码方法来计算能量。但是，单壁编码对于小尺寸的问题很有用。
Black-box optimization has potential in numerous applications such as hyperparameter optimization in machine learning and optimization in design of experiments. Ising machines are useful for binary optimization problems because variables can be represented by a single binary variable of Ising machines. However, conventional approaches using an Ising machine cannot handle black-box optimization problems with non-binary values. To overcome this limitation, we propose an approach for integer-variable black-box optimization problems by using Ising/annealing machines and factorization machines in cooperation with three different integer-encoding methods. The performance of our approach is numerically evaluated with different encoding methods using a simple problem of calculating the energy of the hydrogen molecule in the most stable state. The proposed approach can calculate the energy using any of the integer-encoding methods. However, one-hot encoding is useful for problems with a small size.
