Bayesian optimization (BO) suffers from long computing times when processing highly-dimensional or large data sets. These long computing times are a result of the Gaussian process surrogate model having a polynomial time complexity with the number of experiments. Running BO on high-dimensional or massive data sets becomes intractable due to this time complexity scaling, in turn, hindering experimentation. Alternative surrogate models have been developed to reduce the computing utilization of the BO procedure, however, these methods require mathematical alteration of the inherit surrogate function, pigeonholing use into only that function. In this paper, we demonstrate a generalizable BO wrapper of memory pruning and bounded optimization, capable of being used with any surrogate model and acquisition function. Using this memory pruning approach, we show a decrease in wall-clock computing times per experiment of BO from a polynomially increasing pattern to a sawtooth pattern that has a non-increasing trend without sacrificing convergence performance. Furthermore, we illustrate the generalizability of the approach across two unique data sets, two unique surrogate models, and four unique acquisition functions. All model implementations are run on the MIT Supercloud state-of-the-art computing hardware.

本文介绍了一种可与任何代理模型和获取函数一起使用的可广泛应用于内存修剪与有界优化的贝叶斯优化封装方法，通过这种内存修剪方法，我们展示了贝叶斯优化在每个实验的墙钟计算时间从多项式递增模式转变为具有非递增趋势的锯齿模式，而不牺牲收敛性能。此外，我们证明了该方法在两个独特数据集、两个独特代理模型和四个独特获取函数上的通用性能。

利用可推广的记忆修剪减少贝叶斯优化的计算时间