Feasibility-Driven Trust Region Bayesian Optimization

• URL: http://arxiv.org/abs/2506.14619v1
• Date: Tue, 17 Jun 2025 15:16:22 GMT
• Title: Feasibility-Driven Trust Region Bayesian Optimization
• Authors: Paolo Ascia, Elena Raponi, Thomas Bäck, Fabian Duddeck,
• Abstract summary: FuRBO iteratively defines a trust region from which the next candidate solution is selected.<n>We empirically demonstrate the effectiveness of FuRBO through extensive testing on the full BBOB-constrained benchmark suite.
• Score: 0.048748194765816946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian optimization is a powerful tool for solving real-world optimization tasks under tight evaluation budgets, making it well-suited for applications involving costly simulations or experiments. However, many of these tasks are also characterized by the presence of expensive constraints whose analytical formulation is unknown and often defined in high-dimensional spaces where feasible regions are small, irregular, and difficult to identify. In such cases, a substantial portion of the optimization budget may be spent just trying to locate the first feasible solution, limiting the effectiveness of existing methods. In this work, we present a Feasibility-Driven Trust Region Bayesian Optimization (FuRBO) algorithm. FuRBO iteratively defines a trust region from which the next candidate solution is selected, using information from both the objective and constraint surrogate models. Our adaptive strategy allows the trust region to shift and resize significantly between iterations, enabling the optimizer to rapidly refocus its search and consistently accelerate the discovery of feasible and good-quality solutions. We empirically demonstrate the effectiveness of FuRBO through extensive testing on the full BBOB-constrained COCO benchmark suite and other physics-inspired benchmarks, comparing it against state-of-the-art baselines for constrained black-box optimization across varying levels of constraint severity and problem dimensionalities ranging from 2 to 60.

Related papers

• 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)
• Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints [37.095510211590984]
  We propose BE-CBO, a new Bayesian optimization method that efficiently explores the boundary between feasible and infeasible designs. Our method demonstrates superior performance against state-of-the-art methods through comprehensive experiments on synthetic and real-world benchmarks.
  arXiv  Detail & Related papers  (2024-02-12T14:59:40Z)
• 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)
• Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime [59.27851754647913]
  Predictive optimization is the precise modeling of many real-world applications, including energy cost-aware scheduling and budget allocation on advertising. We develop a modular framework to benchmark 11 existing PtO/PnO methods on 8 problems, including a new industrial dataset for advertising. Our study shows that PnO approaches are better than PtO on 7 out of 8 benchmarks, but there is no silver bullet found for the specific design choices of PnO.
  arXiv  Detail & Related papers  (2023-11-13T13:19:34Z)
• Black-Box Optimization with Implicit Constraints for Public Policy [7.905659620019301]
  This paper introduces a novel BBO framework, termed as the Conditional And Generative Black-box Optimization (CageBO)<n>The CageBO efficiently handles the implicit constraints often found in public policy applications.<n>Our results reveal that our CageBO offers notable improvements in performance and efficiency compared to the baselines.
  arXiv  Detail & Related papers  (2023-10-27T19:47:26Z)
• 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)
• Discovering Many Diverse Solutions with Bayesian Optimization [7.136022698519586]
  We propose Rank-Ordered Bayesian Optimization with Trust-regions (ROBOT) ROBOT aims to find a portfolio of high-performing solutions that are diverse according to a user-specified diversity metric. We show that it can discover large sets of high-performing diverse solutions while requiring few additional function evaluations.
  arXiv  Detail & Related papers  (2022-10-20T01:56:38Z)
• Generalizing Bayesian Optimization with Decision-theoretic Entropies [102.82152945324381]
  We consider a generalization of Shannon entropy from work in statistical decision theory. We first show that special cases of this entropy lead to popular acquisition functions used in BO procedures. We then show how alternative choices for the loss yield a flexible family of acquisition functions.
  arXiv  Detail & Related papers  (2022-10-04T04:43:58Z)
• ARES: An Efficient Algorithm with Recurrent Evaluation and Sampling-Driven Inference for Maximum Independent Set [48.57120672468062]
  This paper introduces an efficient algorithm for the Maximum Independent Set (MIS) problem, incorporating two innovative techniques.<n>The proposed algorithm outperforms state-of-the-art algorithms in terms of solution quality, computational efficiency, and stability.
  arXiv  Detail & Related papers  (2022-08-16T14:39:38Z)
• Sparse Bayesian Optimization [16.867375370457438]
  We present several regularization-based approaches that allow us to discover sparse and more interpretable configurations. We propose a novel differentiable relaxation based on homotopy continuation that makes it possible to target sparsity. We show that we are able to efficiently optimize for sparsity.
  arXiv  Detail & Related papers  (2022-03-03T18:25:33Z)
• Learning Proximal Operators to Discover Multiple Optima [66.98045013486794]
  We present an end-to-end method to learn the proximal operator across non-family problems. We show that for weakly-ized objectives and under mild conditions, the method converges globally.
  arXiv  Detail & Related papers  (2022-01-28T05:53:28Z)
• Outlier-Robust Sparse Estimation via Non-Convex Optimization [73.18654719887205]
  We explore the connection between high-dimensional statistics and non-robust optimization in the presence of sparsity constraints. We develop novel and simple optimization formulations for these problems. As a corollary, we obtain that any first-order method that efficiently converges to station yields an efficient algorithm for these tasks.
  arXiv  Detail & Related papers  (2021-09-23T17:38:24Z)
• Scalable Constrained Bayesian Optimization [10.820024633762596]
  The global optimization of a high-dimensional black-box function under black-box constraints is a pervasive task in machine learning, control, and the scientific community. We propose the scalable constrained Bayesian optimization (SCBO) algorithm that overcomes the above challenges and pushes the state-the-art.
  arXiv  Detail & Related papers  (2020-02-20T01:48:46Z)

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.