# Automatic differentiation of f(x) constructed by decomposition of a matrix M(x)

**URL:** <https://discourse.julialang.org/t/automatic-differentiation-of-f-x-constructed-by-decomposition-of-a-matrix-m-x/33061>\
**Category:** Optimization (Mathematical)\
**Tags:** differentiation, flux, machine-learning, numerics\
**Created:** [January 7, 2020, 12:54pm UTC](https://discourse.julialang.org/t/automatic-differentiation-of-f-x-constructed-by-decomposition-of-a-matrix-m-x/33061 "2020-01-07T12:54:04Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![stevengj](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/stevengj/32/71_2.png) [@stevengj](https://discourse.julialang.org/u/stevengj)\
**Post date:** [January 7, 2020, 1:14pm UTC](https://discourse.julialang.org/t/automatic-differentiation-of-f-x-constructed-by-decomposition-of-a-matrix-m-x/33061/2 "2020-01-07T13:14:21Z")

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See my answer in a previous thread on the same topic: [Native eigenvals for differentiable programming - #9 by stevengj](https://discourse.julialang.org/t/native-eigenvals-for-differentiable-programming/27126/9)

If you can write down exactly what `f(x)` you are computing, maybe there is a way to express it without using eigenvectors.

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