用玻尔兹曼采样实现高效大批次贝叶斯优化，兼顾速度与多样性。
B3O: Scalable Boltzmann Batch Bayesian Optimization
- 将批量生成转化为玻尔兹曼分布采样，避免传统方法瓶颈。
- 在标准测试中性能超越现有方法，多目标电极设计任务中表现稳健。
- 适合需要大规模并行仿真优化的工程场景，如复杂系统配置。
现代工程流程日益依赖大规模并行仿真，推动对可扩展的大批次贝叶斯优化（BO）的需求。然而，现有批量BO方法要么计算成本高，要么依赖近似导致批次多样性下降。本文提出B3O（Boltzmann Batch Bayesian Optimization），将批量生成重新建模为纯采样问题：直接从由采集函数定义的玻尔兹曼分布中采样，避免了现有大规模批量方法的瓶颈。理论上，我们证明从该分布采样的查询仅带来可忽略的额外遗憾。实验上，B3O在标准合成基准上优于现有批量BO方法，并在复杂实际任务中表现出稳健性，包括多目标电极设计和混合变量赛车配置。
原文摘要 · Abstract (English)
Modern engineering workflows increasingly rely on massive parallel simulation, driving the need for scalable, large-batch Bayesian Optimization (BO). Existing batch BO methods, however, incur large computational cost or rely on approximations that erode batch diversity. We propose B3O (Boltzmann Batch Bayesian Optimization), a framework that reframes batch generation as a pure sampling problem: drawing samples directly from the Boltzmann distribution defined by the acquisition function avoids the bottlenecks of existing large-batch methods. Theoretically, we prove that queries sampled from this distribution incur only negligible additional regret. Empirically, B3O outperforms existing batch BO methods on standard synthetic benchmarks and adapts robustly across complex applied tasks, including multi-objective electrode design and mixed-variable race car configuration.
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