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 optimiz
The continuous drive for more efficient and robust optimization algorithms in AI and machine learning necessitates innovations like LAGO, especially as models and objectives become more complex.
This framework offers a significant advancement in balancing exploration and exploitation in complex optimization problems, leading to faster and more reliable convergence for AI models and scientific computations.
The specific integration of Bayesian Optimization with trust region methods via an adaptive competition mechanism refines how global and local search are performed, potentially improving the training and deployment of certain AI systems.
- · AI/ML researchers
- · Developers of complex AI systems
- · Industries relying on expensive simulations
- · Inefficient optimization methodologies
Improved performance and stability in machine learning model training and scientific optimization tasks.
Accelerated development cycles for new AI paradigms and applications that were previously bottlenecked by optimization challenges.
Broader adoption of AI in fields requiring highly precise and robust optimization, leading to economic and scientific breakthroughs.
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Read at arXiv cs.LG