On a probabilistic global optimizer derived from the Walker slice sampling

Aditya Gupta; Souvik Das; Debasish Chatterjee

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arXiv · 2411.03851

On a probabilistic global optimizer derived from the Walker slice sampling

Aditya Gupta·Souvik Das·Debasish Chatterjee

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Abstract

This article presents a zeroth order probabilistic global optimization algorithm -- SwiftNav -- for (not necessarily convex) functions over a compact domain. A discretization procedure is deployed on the compact domain, starting with a small step-size $h > 0$ and subsequently adaptively refining it in the course of a simulated annealing routine utilizing the Walker slice and the Gibbs sampler, in order to identify a set of global optimizers up to good precision. SwiftNav is parallelizable, which helps with scalability as the dimension of decision variables increases. Several numerical experiments are included here to demonstrate the effectiveness and accuracy of SwiftNav in high-dimensional benchmark optimization problems.

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Aditya Gupta, Souvik Das, Debasish Chatterjee. 2024-11-06. On a probabilistic global optimizer derived from the Walker slice sampling. https://arxiv.org/abs/2411.03851

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