Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement

arXiv:2502.10330v4 Announce Type: replace Abstract: Recent advances in diffusion models show promising potential to accelerate nonconvex problem solving by leveraging their multimodality. However, most existing diffusion-based optimization approaches rely on supervised learning and lack a mechanism to enforce constraint satisfaction, which is required in real-world applications. In that case, we investigate and theoretically analyze the inherent problem of supervised diffusion solvers and identify the distributional misalignment problem, i.e., the generated solution distribution often exhibits

Source: arXiv cs.LG — read the full report at the original publisher.

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