Added example using JAX and the scipy interface. #119
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This is nice use of JAX. I think the example would get more visibility if added to the documentation. Would you be willing to move it there? You'll probably have to add jax as a dependency in the CI for the docs to build. |
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@moorepants Done. Is it really necessary to add jax as a dependency for docs? I am quite new to sphinx and I don't really see why it might be needed. |
It may not be, if the examples aren't run or doc-tested. |
moorepants
reviewed
Jul 18, 2021
docs/source/tutorial.rst
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| Problem Interface | ||
| ================= | ||
| Algorithmic Differentation | ||
| -------------------- |
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/home/moorepants/src/cyipopt/docs/source/tutorial.rst:30: WARNING: Title underline too short.
Algorithmic Differentation
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Thanks! I guess our CI is no longer running... |
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As we all know, calculating derivatives by hand can be quite error-prone. During the last months, I often used the JAX library to evaluate gradients and (dense) jacobians via AD (algorithmic differentiation), so I thought it might be an interesting example.