论文标题
高维贝叶斯优化具有约束：施用粉末称重
High-Dimensional Bayesian Optimization with Constraints: Application to Powder Weighing
论文作者
论文摘要
贝叶斯优化有效地优化了黑盒问题中的参数。但是，在有限的试验中，该方法对于高维参数不起作用。通过非线性将其嵌入低维空间，可以有效地探索参数。但是，不能考虑约束。我们提出了将参数分解组合到非线性嵌入中，以考虑在高维贝叶斯优化中考虑已知的平等和未知不平等约束。我们将提出的方法应用于粉末称重任务，作为使用情况。基于实验结果，与手动参数调整相比，提出的方法考虑了约束，并将试验数量减少约66％。
Bayesian optimization works effectively optimizing parameters in black-box problems. However, this method did not work for high-dimensional parameters in limited trials. Parameters can be efficiently explored by nonlinearly embedding them into a low-dimensional space; however, the constraints cannot be considered. We proposed combining parameter decomposition by introducing disentangled representation learning into nonlinear embedding to consider both known equality and unknown inequality constraints in high-dimensional Bayesian optimization. We applied the proposed method to a powder weighing task as a usage scenario. Based on the experimental results, the proposed method considers the constraints and contributes to reducing the number of trials by approximately 66% compared to manual parameter tuning.
