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Structure As Search: Unsupervised Permutation Learning for Combinatorial Optimization

Created by
  • Haebom

Author

Yimeng Min, Carla P. Gomes

Outline

This paper proposes a non-self-regressive framework for solving combinatorial optimization problems without sequential decision-making, applying it to the traveling salesman problem (TSP). By applying a similar transformation to the Hamiltonian cycle, the model learns to approximate the permutation matrix through successive relaxations. This unsupervised learning approach achieves competitive performance with existing heuristic algorithms, demonstrating that the inherent structure of the problem can effectively guide combinatorial optimization without sequential decision-making.

Takeaways, Limitations

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Takeaways:
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A novel non-self-regressive approach to combinatorial optimization problems such as the traveling salesman problem is presented.
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Presents the possibility of finding efficient solutions without sequential decision-making.
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Achieves competitive performance compared to existing heuristic algorithms.
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We present an effective combinatorial optimization method that utilizes the unique structure of the problem.
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Limitations:
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Further research is needed on the generalization performance of the proposed framework.
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Additional performance evaluations are needed for problems of varying size and complexity.
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The possibility of a limit to the accuracy of approximation processes through successive relaxations.
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There is a possibility that the performance is limited to certain problem types.
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