mines-opt-ml/fpo-dys
Operator splitting can be used to design easy-to-train models for predict-and-optimize tasks, which scale effortlessly to problems with thousands of variables.
This project helps operations engineers and supply chain managers make faster, data-driven decisions for complex problems like logistics or resource allocation. It takes contextual data, such as market conditions or customer demand, and uses it to predict optimal parameters for discrete optimization problems, delivering solutions that scale to thousands of variables. It's designed for professionals who need to solve combinatorial problems repeatedly and efficiently.
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Use this if you repeatedly solve large-scale optimization problems (like knapsack or shortest path) where the exact problem parameters aren't known directly, but you have contextual data that can inform them.
Not ideal if your optimization problems are small-scale (tens of variables) or if you directly observe all the parameters needed for your optimization tasks.
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Jan 02, 2025
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