This is the implementation of the paper L-DATR: A Limited-Memory Distributed Asynchronous Trust-Region Method. The code provides a distributed optimization algorithm using the Trust Region method, leveraging the L-BFGS method for efficient Hessian approximation. This approach is suitable for large-scale machine learning and data analysis tasks.
To run the implementation, you can use the following command:
mpirun -n 4 python LDATR.py
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