Abstract
Black-box optimization (BBO) is a framework for searching the optimal solution using only the input-output information of the objective function and is applicable to various scenarios, especially, the case where gradient information is not available. Among BBO methods, sequential model-based optimization (SMBO) is a method which aims for high sample efficiency by combining approximation of the objective function with a surrogate model and decision-making strategies that balance exploration and utilization. Although the objective function is a black box, depending on the application, constraints such as known relationships between input variables may be available as prior knowledge. Using this information can lead to more efficient optimization. In this study, we propose an SMBO method that efficiently handles constraints on objective variables in a discrete search space. The proposed method uses the Tensor-Train (TT) decomposition as a surrogate model and incorporates constraints by adding a penalty term to the loss function of TT decomposition. Numerical experiments show that the proposed method outperforms the conventional discrete BBO method in terms of sample efficiency, confirming the effectiveness of using prior knowledge.
