Error Analysis of Surrogate Models Constructed Through Operations on Submodels
研究了无导数优化中，由多个子模型组合成最终模型时，子模型精度与组合模型精度之间的关系，并提出了一个可用于模型误差分析和算法设计的理论框架。
Model-based methods are popular in derivative-free optimization (DFO). In most of them, a single model function is built to approximate the objective function. This is generally based on the assumption that the objective function is one black box. However, some real-life and theoretical problems show that the objective function may consist of several black boxes. In those problems, the information provided by each black box may not be equal. In this situation, one can build multiple submodels that are then combined to become a final model. In this paper, we analyze the relation between the accuracy of those submodels and the model constructed through their operations. We develop a broad framework that can be used as a theoretical tool in model error analysis and future research in DFO algorithm design. Funding: Y. Chen’s research is partially funded by the MITACS Globalink program. All authors research partially supported by NSERC of Canada Discovery [Grant 2018-03865].