LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization
We introduce LAGO, a LocAl-Global Optimization framework coupling Bayesian Optimization (BO) and gradient-based trust region local refinement through an adaptive competition mechanism for smooth expensive-to-evaluate objective functions with available gradients. At each iteration, global and local optimization strategies independently propose candidate points, and the next evaluation is selected based on predicted improvement. LAGO separates global exploration from local refinement at the proposal level: the BO acquisition function is optimized outside the active trust region, while local candidates are proposed within the trust region. Points in the vicinity of the accepted local step ar...
| Authors | Eliott Van Dieren, Tommaso Vanzan, Fabio Nobile |
|---|---|
| Primary category | cs.LG |
| Published | 3 March 2026 |
| Last updated at source | 5 June 2026 |
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