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

• URL: http://arxiv.org/abs/2603.02970v1
• Date: Tue, 03 Mar 2026 13:24:36 GMT
• Title: LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization
• Authors: Eliott Van Dieren, Tommaso Vanzan, Fabio Nobile,
• Abstract summary: We introduce LAGO, a LocAl-Global Optimization algorithm that combines gradient-enhanced Bayesian Optimization and gradient-based trust region local refinement.<n>At each iteration, global and local optimization strategies independently propose candidate points, and the next evaluation is selected based on predicted improvement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 optimization algorithms for smooth functions, while maintaining fast local convergence in regions of interest.

Related papers

• Improving LLM-based Global Optimization with Search Space Partitioning [40.61249592871905]
  Large Language Models (LLMs) have emerged as effective surrogate models and candidate generators within global optimization frameworks.<n>We propose HOLLM, a novel global optimization algorithm that enhances LLM-driven sampling by partitioning the search space into promising subregions.<n> Empirical evaluation on standard optimization benchmarks shows that HOLLM consistently matches or surpasses leading Bayesian optimization and trust-region methods.
  arXiv  Detail & Related papers  (2025-05-27T16:01:49Z)
• Localized Zeroth-Order Prompt Optimization [54.964765668688806]
  We propose a novel algorithm, namely localized zeroth-order prompt optimization (ZOPO) ZOPO incorporates a Neural Tangent Kernel-based derived Gaussian process into standard zeroth-order optimization for an efficient search of well-performing local optima in prompt optimization. Remarkably, ZOPO outperforms existing baselines in terms of both the optimization performance and the query efficiency.
  arXiv  Detail & Related papers  (2024-03-05T14:18:15Z)
• High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy [16.521207412129833]
  We propose a novel technique for defining the local regions using the Covariance Matrix Adaptation (CMA) strategy. Based on this search distribution, we then define the local regions consisting of data points with high probabilities of being the global optimum. Our approach serves as a meta-algorithm as it can incorporate existing black-box BOs, such as BO, TuRBO, and BAxUS, to find the global optimum.
  arXiv  Detail & Related papers  (2024-02-05T15:32:10Z)
• LABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regions [0.0]
  We propose the LABCAT algorithm, which extends trust-region-based BO. We show that the algorithm outperforms several state-of-the-art BO and other black-box optimization algorithms.
  arXiv  Detail & Related papers  (2023-11-19T13:56:24Z)
• Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup [90.26270347459915]
  We propose a novel momentum-based algorithm via utilizing the global descent locally adaptive. textitLADA could greatly reduce the communication rounds and achieves higher accuracy than several baselines.
  arXiv  Detail & Related papers  (2023-07-30T14:53:21Z)
• Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation [84.0621253654014]
  We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest. We show theoretically that BALLET can efficiently shrink the search space, and can exhibit a tighter regret bound than standard BO.
  arXiv  Detail & Related papers  (2023-07-25T09:45:47Z)
• The Behavior and Convergence of Local Bayesian Optimization [20.568490114736818]
  Local optimization strategies can deliver strong empirical performance on high-dimensional problems compared to traditional global strategies. We first study the behavior of the local approach, and find that the statistics of individual local solutions of Gaussian process sample paths are surprisingly good compared to what we would expect to recover from global methods.
  arXiv  Detail & Related papers  (2023-05-24T21:11:49Z)
• Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape [59.841889495864386]
  In federated learning (FL), a cluster of local clients are chaired under the coordination of a global server. Clients are prone to overfit into their own optima, which extremely deviates from the global objective. ttfamily FedSMOO adopts a dynamic regularizer to guarantee the local optima towards the global objective. Our theoretical analysis indicates that ttfamily FedSMOO achieves fast $mathcalO (1/T)$ convergence rate with low bound generalization.
  arXiv  Detail & Related papers  (2023-05-19T10:47:44Z)
• Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning [58.79085525115987]
  Local methods are one of the promising approaches to reduce communication time. We show that the communication complexity is better than non-local methods when the local datasets is smaller than the smoothness local loss.
  arXiv  Detail & Related papers  (2022-02-12T15:12:17Z)
• TREGO: a Trust-Region Framework for Efficient Global Optimization [63.995130144110156]
  We propose and analyze a trust-region-like EGO method (TREGO) TREGO alternates between regular EGO steps and local steps within a trust region. Our algorithm enjoys strong global convergence properties, while departing from EGO only for a subset of optimization steps.
  arXiv  Detail & Related papers  (2021-01-18T00:14:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.