aiqbench analysis
LAGO presents a novel optimization framework that adaptively balances global Bayesian exploration with local trust-region refinement to handle expensive-to-evaluate functions. By decoupling proposal mechanisms and filtering local updates based on lengthscale criteria, the method aims to mitigate numerical instability while improving convergence efficiency. This approach is particularly relevant for practitioners working with smooth objective functions where gradient information is available but function evaluations are costly.
LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization
arXiv:2603.02970v2 Announce Type: replace Abstract: 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 are incorporated in the global GP dataset only when satisfying a lengthscale-based minimum-distance criterion, hence reducing the risk of numerical instability during local exploitation. LAGO enhances BO with efficient local refinement when reaching promising regions, and reverts to exploratory behavior when local steps are not competitive.
Abstract: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 are incorporated in the global GP dataset only when satisfying a lengthscale-based minimum-distance criterion, hence reducing the risk of numerical instability during local exploitation. LAGO enhances BO with efficient local refinement when reaching promising regions, and reverts to exploratory behavior when local steps are not competitive.
Machine Learning (cs.LG); Optimization and Control (math.OC)
arXiv:2603.02970 [cs.LG]
(or arXiv:2603.02970v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2603.02970 Focus to learn more arXiv-issued DOI via DataCite
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