# Efficient Vector^T \* Jacobian matrix?

**URL:** <https://discourse.julialang.org/t/efficient-vector-t-jacobian-matrix/21329>\
**Category:** Numerics\
**Created:** [March 1, 2019, 5:42am UTC](https://discourse.julialang.org/t/efficient-vector-t-jacobian-matrix/21329 "2019-03-01T05:42:23Z")\
**Posts on this page:** 1\
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**Author:** ![antoine-levitt](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/antoine-levitt/32/4008_2.png) [@antoine-levitt](https://discourse.julialang.org/u/antoine-levitt)\
**Post date:** [March 1, 2019, 6:49am UTC](https://discourse.julialang.org/t/efficient-vector-t-jacobian-matrix/21329/2 "2019-03-01T06:49:03Z")

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This is exactly what reverse-mode automatic differentiation is (e.g. if f is R^n to R you’re asking for a O(1) gradient). There are a few packages doing it, but it’s more tricky than forward diff.

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