Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization

Understand

In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion.

• Multi-objective Bayesian optimization (BO) is a common approach, but many of the best-performing acquisition functions do not have known analytic gradients and suffer from high computational overhead.
• We leverage recent advances in programming models and hardware acceleration for multi-objective BO using Expected Hypervolume Improvement (EHVI)---an algorithm notorious for its high computational complexity.
• We derive a novel formulation of q-Expected Hypervolume Improvement (qEHVI), an acquisition function that extends EHVI to the parallel, constrained evaluation setting.