lua-torch-optim - 0~20171127-ga5ceed7-1 main

This package contains several optimization routines and a logger for Torch.
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The following algorithms are provided:
* Stochastic Gradient Descent
* Averaged Stochastic Gradient Descent
* L-BFGS
* Congugate Gradients
* AdaDelta
* AdaGrad
* Adam
* AdaMax
* FISTA with backtracking line search
* Nesterov's Accelerated Gradient method
* RMSprop
* Rprop
* CMAES
All these algorithms are designed to support batch optimization as well
as stochastic optimization. It's up to the user to construct an objective
function that represents the batch, mini-batch, or single sample on which
to evaluate the objective.
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This package provides also logging and live plotting capabilities via the
`optim.Logger()` function. Live logging is essential to monitor the
network accuracy and cost function during training and testing, for
spotting under- and over-fitting, for early stopping or just for monitoring
the health of the current optimisation task.