mlsanityData to 7 Oct 2026

TREGO: a Trust-Region Framework for Efficient Global Optimization

arXiv 2101.06808 ↗First posted 18 Jan 2021math.OC stat.MLcited by 28

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Abstract (arXiv)

Efficient Global Optimization (EGO) is the canonical form of Bayesian optimization that has been successfully applied to solve global optimization of expensive-to-evaluate black-box problems. However, EGO struggles to scale with dimension, and offers limited theoretical guarantees. In this work, a trust-region framework for EGO (TREGO) is proposed and analyzed. TREGO alternates between regular EGO steps and local steps within a trust region. By following a classical scheme for the trust region (based on a sufficient decrease condition), the proposed algorithm enjoys global convergence properties, while departing from EGO only for a subset of optimization steps. Using extensive numerical experiments based on the well-known COCO {bound constrained problems}, we first analyze the sensitivity of TREGO to its own parameters, then show that the resulting algorithm is consistently outperforming EGO and getting competitive with other state-of-the-art black-box optimization methods.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 14 of its 63 references that are in the map. First posted on arXiv 18 Jan 2021.

Builds on

14 of 63 references.

References · 14

  1. 2018A Tutorial on Bayesian Optimizationcited by 734
  2. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  3. 2013Bayesian Optimization in a Billion Dimensions via Random Embeddingscited by 178
  4. 2015High Dimensional Bayesian Optimisation and Bandits via Additive Modelscited by 137
  5. 2016COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Settingcited by 135
  6. 2018BOCK : Bayesian Optimization with Cylindrical Kernelscited by 62
  7. 2016Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangiancited by 29
  8. 2015Stochastic Optimization Using a Trust-Region Method and Random Modelscited by 25
  9. 2018Optimization, fast and slow: optimally switching between local and Bayesian optimizationcited by 22
  10. 2017Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observationscited by 11
  11. 2018A stochastic Levenberg-Marquardt method using random models with complexity resultscited by 5
  12. 2015A Taxonomy of Constraints in Simulation-Based Optimizationcited by 4
2 more
  1. 2018A Merit Function Approach for Evolution Strategiescited by 2
  2. 2019StoMADS: Stochastic blackbox optimization using probabilistic estimatescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 8; 2024: 2; 2025: 3; 2026: 9.

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  1. 2023Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisationcited by 32
  2. 2022Local Bayesian optimization via maximizing probability of descentcited by 29
  3. 2024A survey and benchmark of high-dimensional Bayesian optimization of discrete sequencescited by 22
  4. 2023Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOBcited by 18
  5. 2022Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)cited by 14
  6. 2023Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowcited by 11
  7. 2021Revisiting Bayesian Optimization in the light of the COCO benchmarkcited by 10
  8. 2023High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraftcited by 8
  9. 2023HypBO: Accelerating Black-Box Scientific Experiments Using Experts' Hypothesescited by 6
  10. 2023Mastering the exploration-exploitation trade-off in Bayesian Optimizationcited by 3
  11. 2022Revisiting Architecture-aware Knowledge Distillation: Smaller Models and Faster Searchcited by 2
  12. 2022Bayesian Optimization with Informative Covariancecited by 2
16 more
  1. 2023LABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regionscited by 2
  2. 2025High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddingscited by 2
  3. 2026Do We Really Need to Approach the Entire Pareto Front in Many-Objective Bayesian Optimisation?cited by 2
  4. 2023A Principle for Global Optimization with Gradientscited by 1
  5. 2022Approximate Nash Equilibrium Learning for n-Player Markov Games in Dynamic Pricingcited by 0
  6. 2024SCORE: A 1D Reparameterization Technique to Break Bayesian Optimization's Curse of Dimensionalitycited by 0
  7. 2025Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effectscited by 0
  8. 2025Adaptive Replication Strategies in Trust-Region-Based Bayesian Optimization of Stochastic Functionscited by 0
  9. 2026GimmBO: Interactive Generative Image Model Merging via Bayesian Optimizationcited by 0
  10. 2026LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimizationcited by 0
  11. 2026Local Constrained Bayesian Optimizationcited by 0
  12. 2026B3O: Scalable Boltzmann Batch Bayesian Optimizationcited by 0
  13. 2026Efficient multidisciplinary design via Bayesian optimizationcited by 0
  14. 2026GRAPE: Gradient Refinement and Progress-Aware Exploitation for Query-Efficient High-Dimensional Bayesian Optimizationcited by 0
  15. 2026Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisationcited by 0
  16. 2026MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimizationcited by 0