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Optimizers for/and sklearn compatible Machine Learning models

License: MIT License

Python 100.00%
artificial-intelligence constrained-optimization convex-optimization deep-neural-networks duality lagrangian-relaxation machine-learning neural-network nonlinear-optimization nonsmooth-optimization numerical-optimization optimization optimization-algorithms optimizers quadratic-programming sklearn-compatible svm unconstrained-optimization

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optiml's Issues

About nesterov form

Hi,
In stochastic gradient algorithm line 74---76, you write:
correction = next(self.step_size(*batch)) * d
self.x += correction
self.step = jump + correction

Whether self.x += self.step is better?
Thanks,

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