# mx.nd.nag.mom.update¶

## Description¶

Update function for Nesterov Accelerated Gradient( NAG) optimizer. It updates the weights using the following formula,

$\begin{split}v_t = \gamma v_{t-1} + \eta * \nabla J(W_{t-1} - \gamma v_{t-1})\\ W_t = W_{t-1} - v_t\end{split}$

Where $$\eta$$ is the learning rate of the optimizer $$\gamma$$ is the decay rate of the momentum estimate $$\v_t$$ is the update vector at time step t $$\W_t$$ is the weight vector at time step t

## Arguments¶

Argument

Description

weight

NDArray-or-Symbol.

Weight

grad

NDArray-or-Symbol.

mom

NDArray-or-Symbol.

Momentum

lr

float, required.

Learning rate

momentum

float, optional, default=0.

The decay rate of momentum estimates at each epoch.

wd

float, optional, default=0.

Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.

rescale.grad

float, optional, default=1.

clip.gradient
out The result mx.ndarray