Yuma-Ichikawa/CRA4CO
A PyTorch implementation: Controlling Continuous Relaxation for Combinatorial Optimization
This project offers a PyTorch implementation for solving complex combinatorial optimization problems, which involve finding the best solution from a finite set of possibilities (like scheduling or resource allocation). It takes a problem definition, often represented as a graph, and outputs a refined solution that is more accurate and efficient than other unsupervised learning methods. This tool is designed for researchers and practitioners working on advanced optimization techniques.
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Use this if you are a researcher or advanced practitioner developing unsupervised learning-based solvers for combinatorial optimization and need to improve solution quality and accelerate the training process.
Not ideal if you are looking for an off-the-shelf, easy-to-use solver for standard optimization problems without deep learning expertise.
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Jupyter Notebook
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BSD-3-Clause-Clear
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Last pushed
Mar 20, 2025
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