@@ -29,8 +29,7 @@ kernelspec:
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29 | 29 | ``` |
30 | 30 | |
31 | 31 | ```{seealso} |
32 | | -**GPU:** A version of this lecture which makes use of [jax](https://jax.readthedocs.io) to run the code |
33 | | -on a `GPU` is [available here](https://jax.quantecon.org/newtons_method.html) |
| 32 | +A version of this lecture using [JAX](https://github.com/jax-ml/jax) is {doc}`available here <jax:newtons_method>` |
34 | 33 | ``` |
35 | 34 | |
36 | 35 | ## Overview |
@@ -788,7 +787,7 @@ With the larger overhead, the speed is not better than the optimized `scipy` fun
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788 | 787 | Our next step is to investigate a large market with 3,000 goods. |
789 | 788 | |
790 | 789 | A JAX version of this section using GPU accelerated linear algebra and |
791 | | -automatic differentiation is available [here](https://jax.quantecon.org/newtons_method.html#application) |
| 790 | +automatic differentiation is {doc}`available here <jax:newtons_method>` |
792 | 791 | |
793 | 792 | The excess demand function is essentially the same, but now the matrix $A$ is $3000 \times 3000$ and the parameter vectors $b$ and $c$ are $3000 \times 1$. |
794 | 793 | |
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