Clip by Norm, Then Take One Step

A two-parameter model has loss

$L(w_1, w_2) = (w_1 w_2 - 5)^2$

and the current parameters are $(w_1, w_2) = (4, 3)$.

Training applies clipping by norm with threshold $c = 10$: if $\lVert \nabla L \rVert > c$, the gradient is replaced by

$\frac{c}{\lVert \nabla L \rVert}\,\nabla L$

and otherwise left alone. The resulting gradient is then used in an ordinary update with $\alpha = 0.5$.

What are the new parameter values after this single update?