One Step Downhill

A tiny model has two weights, $w_1$ and $w_2$, and its loss is

$L(w_1, w_2) = (w_1 - 3)^2 + 2(w_2 + 1)^2$

Gradient descent updates every weight at once using the rule

$\mathbf{w}_{\text{new}} = \mathbf{w} - \alpha\,\nabla L(\mathbf{w})$

where $\alpha$ is the learning rate, a small positive number controlling the step size.

Starting from $\mathbf{w} = (5,\ 1)$ with $\alpha = 0.1$, what is $\mathbf{w}_{\text{new}}$?