[2603.02970] LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization

Computer Science > Machine Learning arXiv:2603.02970 (cs) [Submitted on 3 Mar 2026] Title:LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization Authors:Eliott Van Dieren, Tommaso Vanzan, Fabio Nobile View a PDF of the paper titled LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization, by Eliott Van Dieren and 1 other authors View PDF HTML (experimental) Abstract:We introduce LAGO, a LocAl-Global Optimization algorithm that combines gradient-enhanced Bayesian Optimization (BO) with gradient-based trust region local refinement through an adaptive competition mechanism. 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 function and gradient evaluations are incorporated into the global gradient-enhanced Gaussian process only when they satisfy a lengthscale-based minimum-distance criterion, reducing the risk of numerical instability during the local exploitation. This enables efficient local refinement when reaching promising regions, without sacrificing a global search of the design space. As a result, the method achieves an improved exploration of the full design space compared to standard non-linear local...