B3O: Scalable Boltzmann Batch Bayesian Optimization

2026-06-29

AI Summary

This work addresses the high computational cost and limited batch diversity in large-scale parallel Bayesian optimization by reframing batch generation as a pure sampling problem—specifically, direct sampling from the Boltzmann distribution induced by the acquisition function. This approach circumvents the computational bottlenecks inherent in conventional large-batch optimization schemes. Theoretically, the sampled points introduce only negligible additional regret while ensuring high diversity. Empirical evaluations demonstrate that the proposed method outperforms existing techniques on standard synthetic benchmarks and exhibits superior performance and robustness in complex real-world tasks, including multi-objective electrode design and mixed-variable racecar configuration.

Abstract

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.

Problem

• Bayesian Optimization
• batch diversity
• scalable optimization
• massive parallel simulation
• acquisition function

Innovation

• Boltzmann sampling
• batch Bayesian optimization
• large-batch BO
• acquisition function
• scalable optimization