Generalizing Optimization Models
Thibaut discusses the exciting potential of creating universal models that generalize across different cities and scales. By leveraging end-to-end learning and a surrogate problem approach, the method allows for effective optimization without needing prior knowledge of optimal solutions. This innovative strategy not only enhances scalability but also integrates combinatorial optimization directly into the learning process.In this clip
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