Local Constrained Bayesian Optimization
Bayesian optimization (BO) for high-dimensional constrained problems remains a significant challenge due to the curse of dimensionality. We propose Local Constrained Bayesian Optimization (LCBO), a novel framework tailored for such settings. Unlike trust-region methods that are prone to premature shrinking when confronting tight or complex constraints, LCBO leverages the differentiable landscape of constraint-penalized surrogates to alternate between rapid local descent and uncertainty-driven exploration. Theoretically, we prove that LCBO achieves a convergence rate for the Karush-Kuhn-Tucker (KKT) residual that depends polynomially on the dimension $d$ for common kernels under mild assum...
| Authors | Jing Jingzhe, Fan Zheyi, Szu Hui Ng et al. |
|---|---|
| Primary category | stat.ML |
| Published | 9 March 2026 |
| Last updated at source | 3 July 2026 |
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