Table Of Contents
Table Of Contents


mxnet.ndarray.sparse.adagrad_update(weight=None, grad=None, history=None, lr=_Null, epsilon=_Null, wd=_Null, rescale_grad=_Null, clip_gradient=_Null, out=None, name=None, **kwargs)

Update function for AdaGrad optimizer.

Referenced from Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, and available at

Updates are applied by:

rescaled_grad = clip(grad * rescale_grad, clip_gradient)
history = history + square(rescaled_grad)
w = w - learning_rate * rescaled_grad / sqrt(history + epsilon)

Note that non-zero values for the weight decay option are not supported.

Defined in src/operator/

  • weight (NDArray) – Weight

  • grad (NDArray) – Gradient

  • history (NDArray) – History

  • lr (float, required) – Learning rate

  • epsilon (float, optional, default=1e-07) – epsilon

  • wd (float, optional, default=0) – weight decay

  • rescale_grad (float, optional, default=1) – Rescale gradient to grad = rescale_grad*grad.

  • clip_gradient (float, optional, default=-1) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).

  • out (NDArray, optional) – The output NDArray to hold the result.


out – The output of this function.

Return type

NDArray or list of NDArrays