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i wrote counterparts to the existing `finite_difference_jacobian` for the case where the function of interest is analytically differentiable. happy to submit a PR if there is interest. would just ne…
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```julia
using Symbolics
@variables x y
z = x+y
Symbolics.jacobian([x*z],[x, z])
```
Expected
[x+y x]
Got
[y + 2x x]
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Hi:
In IMUError.cpp, const double sigma2_p = 0.5 * dt * dt * sigma2_v;
is const double sigma2_p = 0.25 * dt * dt * sigma2_v?
Thank you
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### 🚀 The feature, motivation and pitch
Neural Nets are often applied in physical or related fields. Here, one usually not only need an output, put also its derivative. With Pytorch one can use Aut…
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When I use GTSAM, build the factors that optimize for Pose3. Taking the variant of GPSFactor as an example, the residual is as follows.
`residual = R * p.translation() – nT_;`
I'm going to change GP…
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I would like to add the Jacobian matrix to this package. Do you have an opinion on how to implement it? It's the partial derivative of a function evaluated at a variety of X values, with respect to ea…
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I'm looking to take two jacobians of the network neural network (with respect to its inputs) and then do parameter based optimization. Up front, I want to thank you for taking the time to read this, I…
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I am trying to find out how to expose the Jacobian and linear solver choices in the common interface and I can't find it. From the docs:
https://github.com/sdwfrost/liblsoda/blob/master/orig/lsoda-…
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Sometimes the compilation overhead of symbolic jacobian computation is too high and a different AD framework (e.g. ForwardDiff) would be preferred (at the expense of some runtime overhead).
The use…
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Hey! :wave: I'm trying to use Hessian approximation, but setting it like (with JuMP)
```julia
m = Model(()->MadNLP.Optimizer(hessian_approximation=MadNLP.CompactLBFGS));
```
has no effect, it stil…