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odeint has everything necessary to generate a numerical jacobian. We are dealing with some systems with very complex right hand side so we don't calculate the jacobian analytically. I'm currently ge…
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Hello! I am interested in using Polyster.jl to evaluate Jacobians and Hessians in parallel, similar to `threaded_gradient!` over [here](https://github.com/JuliaSIMD/Polyester.jl/blob/master/src/forwar…
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Let H be a matrix, x a vector, and f! a matrix-valued function. To make this fit the interface, I allow H to be a vector and reshape H :
```
using SparseArrays, SparseDiffTools, BenchmarkTools, Prof…
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**Issue by [bob-carpenter](https://github.com/bob-carpenter)**
_Thursday Jan 30, 2014 at 22:14 GMT_
_Originally opened as https://github.com/stan-dev/stan/issues/524_
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Vectorize all the Jacobia…
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Hello,
I would like to compute the inner product J * Sigma * J^T, where J is the Jacobian of a neural network and Sigma is a diagonal matrix of size p by p, where p is the number of network paramet…
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Allow FitBenchmarking to access the native Jacobians (analytic or otherwise) computed by Mantid.
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We have user-defined dense Jacobians. Now we can use that to setup user-defined banded Jacobians. Ping me if you want this implemented.
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MWE:
```julia
using OrdinaryDiffEq
tspan = (0.0,1.0)
u0 = zeros(1)
u0[1] = 1/2
function residual(res,du,u,p,t)
res[1] = - 1.01u[1] + du[1]
end
function jacobian(jac,du,u,p,gamma,t)
…
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As Sean and I discussed, we should probably auto-generate the analytical jacobian functions instead of relying on numerical computations. This will eliminate one level of numerics used.
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Vectorize all the Jacobians. They should already be analytic, but if they're not, do that, too.