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I'm looking for fast Python implémentations of gradient descent optimization algorithm. I have a convex problem , with no constraint, so for now I'm using the BFGS algorithm implemented in scikit-learn ( minimize ).

Is there anything faster / scalable on multi-core systems ?

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Parallel gradient descent has been implemented in this repository in Python. It should have a familiar interface, since it's being developed for implementation as a scikit-learn feature.

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