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This is a Quantitative Study study.

September 29, 2025Open Access

Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization

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Learning-based approach improves combinatorial optimization solutions using graph neural networks, indicating better performance.

The proposed method enhances approximation algorithms for combinatorial optimization, achieving near-optimal solutions.
Competitive performance is demonstrated on well-known combinatorial optimization problems like the travelling salesman problem.
A novel gradient estimation method treats the approximation algorithm as a black box, allowing for self-supervised training.
Graph neural networks are used to predict parameter values, improving the efficiency and effectiveness of the optimization process.

Mielke et al. (2025) studied this question.

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