A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization

• URL: http://arxiv.org/abs/2406.01661v2
• Date: Thu, 8 Aug 2024 12:17:56 GMT
• Title: A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
• Authors: Sebastian Sanokowski, Sepp Hochreiter, Sebastian Lehner,
• Abstract summary: Current deep learning approaches rely on generative models that yield exact sample likelihoods. This work introduces a method that lifts this restriction and opens the possibility to employ highly expressive latent variable models. We experimentally validate our approach in data-free Combinatorial Optimization and demonstrate that our method achieves a new state-of-the-art on a wide range of benchmark problems.
• Score: 7.378582040635655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning to sample from intractable distributions over discrete sets without relying on corresponding training data is a central problem in a wide range of fields, including Combinatorial Optimization. Currently, popular deep learning-based approaches rely primarily on generative models that yield exact sample likelihoods. This work introduces a method that lifts this restriction and opens the possibility to employ highly expressive latent variable models like diffusion models. Our approach is conceptually based on a loss that upper bounds the reverse Kullback-Leibler divergence and evades the requirement of exact sample likelihoods. We experimentally validate our approach in data-free Combinatorial Optimization and demonstrate that our method achieves a new state-of-the-art on a wide range of benchmark problems.

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