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arXiv · 2511.19241

Local Entropy Search over Descent Sequences for Bayesian Optimization

David Stenger·Armin Lindicke·Alexander von Rohr·Sebastian Trimpe
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Abstract

Searching large and complex design spaces for a global optimum can be infeasible and unnecessary. A practical alternative is to iteratively refine the neighborhood of an initial design using local optimization methods such as gradient descent. We propose local entropy search (LES), a Bayesian optimization paradigm that explicitly targets the solutions reachable by the descent sequences of iterative optimizers. The algorithm propagates the posterior belief over the objective through the optimizer, resulting in a probability distribution over descent sequences. It then selects the next evaluation by maximizing mutual information with that distribution, using a combination of analytic entropy calculations and Monte-Carlo sampling of descent sequences. Empirical results on high-complexity synthetic objectives and benchmark problems show that LES achieves strong sample efficiency compared to existing local and global Bayesian optimization methods.

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David Stenger, Armin Lindicke, Alexander von Rohr, Sebastian Trimpe. 2025-11-24. Local Entropy Search over Descent Sequences for Bayesian Optimization. https://arxiv.org/abs/2511.19241

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