The content discusses a novel approach to Bayesian optimization by limiting the search region to lower dimensions and utilizing local Gaussian process regression (LGPR). This method improves prediction accuracy and reduces time complexity, leading to increased search efficiency. Evaluation results show significant improvements in search efficiency compared to traditional methods.
The paper introduces BOLDUC, which includes LineBO as a component, focusing on improving search efficiency through dimensionality reduction. The proposed method extracts a local subset of data specific to the low-dimensional search region, enhancing prediction accuracy and reducing computational complexity. Evaluation experiments demonstrate superior performance compared to standard Bayesian optimization methods.
Key points include the introduction of BOLDUC for efficient optimization tasks, the use of LGPR for improved prediction accuracy in low-dimensional spaces, and the reduction of matrix inversion time complexity. The study evaluates BOLDUC using benchmark functions like Ackley and Rosenbrock functions, showcasing its effectiveness in improving search efficiency.
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arxiv.org
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