Diffusion models are used in DIFFOPT to address optimization problems with unknown constraints. The method involves a two-stage framework, combining guided diffusion and Langevin dynamics, to achieve better performance in various optimization tasks.
Addressing real-world optimization challenges where constraints are unknown is crucial. DIFFOPT leverages diffusion models to learn feasible spaces from data, enhancing sampling efficiency and achieving competitive performance across different datasets.
Previous studies have identified issues with unknown objective functions but limited research has focused on scenarios without explicit analytic constraints. Overlooking these feasibility constraints during optimization can lead to unrealistic solutions in practice.
DIFFOPT's innovative approach of utilizing diffusion models for constrained optimization shows promising results across synthetic and real-world datasets. By learning the data distribution and integrating it into the sampling process, the method achieves better or comparable performance compared to state-of-the-art baselines.
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by Lingkai Kong... klo arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.18012.pdfSyvällisempiä Kysymyksiä